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Oktian, Yustus Eko, Brian Stanley, and Sang-Gon Lee. "Building Trusted Federated Learning on Blockchain." Symmetry 14, no. 7 (July 8, 2022): 1407. http://dx.doi.org/10.3390/sym14071407.
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Federated learning enables multiple users to collaboratively train a global model using the users’ private data on users’ local machines. This way, users are not required to share their training data with other parties, maintaining user privacy; however, the vanilla federated learning proposal is mainly assumed to be run in a trusted environment, while the actual implementation of federated learning is expected to be performed in untrusted domains. This paper aims to use blockchain as a trusted federated learning platform to realize the missing “running on untrusted domain” requirement. First, we investigate vanilla federate learning issues such as client’s low motivation, client dropouts, model poisoning, model stealing, and unauthorized access. From those issues, we design building block solutions such as incentive mechanism, reputation system, peer-reviewed model, commitment hash, and model encryption. We then construct the full-fledged blockchain-based federated learning protocol, including client registration, training, aggregation, and reward distribution. Our evaluations show that the proposed solutions made federated learning more reliable. Moreover, the proposed system can motivate participants to be honest and perform best-effort training to obtain higher rewards while punishing malicious behaviors. Hence, running federated learning in an untrusted environment becomes possible.
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Li, Yanbin, Yue Li, Huanliang Xu, and Shougang Ren. "An Adaptive Communication-Efficient Federated Learning to Resist Gradient-Based Reconstruction Attacks." Security and Communication Networks 2021 (April 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/9919030.
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The widely deployed devices in Internet of Things (IoT) have opened up a large amount of IoT data. Recently, federated learning emerges as a promising solution aiming to protect user privacy on IoT devices by training a globally shared model. However, the devices in the complex IoT environments pose great challenge to federate learning, which is vulnerable to gradient-based reconstruction attacks. In this paper, we discuss the relationships between the security of federated learning model and optimization technologies of decreasing communication overhead comprehensively. To promote the efficiency and security, we propose a defence strategy of federated learning which is suitable to resource-constrained IoT devices. The adaptive communication strategy is to adjust the frequency and parameter compression by analysing the training loss to ensure the security of the model. The experiments show the efficiency of our proposed method to decrease communication overhead, while preventing privacy data leakage.
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Bektemyssova, G. U., G. S. Bakirova, Sh G. Yermukhanbetova, A. Shyntore, D. B. Umutkulov, and Zh S. Mangysheva. "Analysis of the relevance and prospects of application of federate training." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 92, no. 2 (June 30, 2024): 56–65. http://dx.doi.org/10.47533/2024.1606-146x.26.
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This article examines federated learning (FOE) as an innovative approach to machine learning, different from traditional methods. In conventional machine learning (MO), data is collected on a central server to train the model. However, in the case of FO, the learning model is directed to data distributed across local devices, and learning takes place directly on these devices. In addition, the article discusses methods and algorithms of federated learning, identifies the advantages and real areas of application of federated learning. FO is used in various fields, including working with medical data and personal data of customers in sales companies. This approach is especially valuable for ensuring data confidentiality and privacy.
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Shkurti, Lamir, and Mennan Selimi. "AdaptiveMesh: Adaptive Federate Learning for Resource-Constrained Wireless Environments." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 14 (November 14, 2024): 22–37. http://dx.doi.org/10.3991/ijoe.v20i14.50559.
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Federated learning (FL) presents a decentralized approach to model training, particularly beneficial in scenarios prioritizing data privacy, such as healthcare. This paper introduces AdaptiveMesh, an FL adaptive algorithm designed to optimize training efficiency in heterogeneous wireless environments. Through dynamic adjustment of training parameters based on client performance metrics, including central processing unit (CPU) utilization and accuracy trends, AdaptiveMesh aims to enhance model convergence and resource utilization. Experimental evaluations on heterogeneous client devices demonstrate the algorithm’s effectiveness in improving model accuracy, stability, and training efficiency. Results indicate a significant impact on CPU adaptation in preventing client overloading and mitigating overheating risks. Furthermore, the results of the one-way analysis of variance (ANOVA) and regression analysis highlight significant differences in CPU usage, accuracy, and epochs between devices with varying levels of hardware capabilities. These findings underscore the algorithm’s potential for practical deployment in real-world edge computing environments, addressing challenges posed by heterogeneous device capabilities and resource constraints.
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Kholod, Ivan, Evgeny Yanaki, Dmitry Fomichev, Evgeniy Shalugin, Evgenia Novikova, Evgeny Filippov, and Mats Nordlund. "Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis." Sensors 21, no. 1 (December 29, 2020): 167. http://dx.doi.org/10.3390/s21010167.
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The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis are not always applicable due to the large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. FL is being actively developed, and currently, there are several open-source frameworks that implement it. This article presents a comparative review and analysis of the existing open-source FL frameworks, including their applicability in IoT systems. The authors evaluated the following features of the frameworks: ease of use and deployment, development, analysis capabilities, accuracy, and performance. Three different data sets were used in the experiments—two signal data sets of different volumes and one image data set. To model low-power IoT devices, computing nodes with small resources were defined in the testbed. The research results revealed FL frameworks that could be applied in the IoT systems now, but with certain restrictions on their use.
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Srinivas, C., S. Venkatramulu, V. Chandra Shekar Rao, B. Raghuram, K. Vinay Kumar, and Sreenivas Pratapagiri. "Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (June 13, 2023): 517–27. http://dx.doi.org/10.17762/ijritcc.v11i6s.6960.
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Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity. Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput.
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Tabaszewski, Maciej, Paweł Twardowski, Martyna Wiciak-Pikuła, Natalia Znojkiewicz, Agata Felusiak-Czyryca, and Jakub Czyżycki. "Machine Learning Approaches for Monitoring of Tool Wear during Grey Cast-Iron Turning." Materials 15, no. 12 (June 20, 2022): 4359. http://dx.doi.org/10.3390/ma15124359.
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The dynamic development of new technologies enables the optimal computer technique choice to improve the required quality in today’s manufacturing industries. One of the methods of improving the determining process is machine learning. This paper compares different intelligent system methods to identify the tool wear during the turning of gray cast-iron EN-GJL-250 using carbide cutting inserts. During these studies, the experimental investigation was conducted with three various cutting speeds vc (216, 314, and 433 m/min) and the exact value of depth of cut ap and federate f. Furthermore, based on the vibration acceleration signals, appropriate measures were developed that were correlated with the tool condition. In this work, machine learning methods were used to predict tool condition; therefore, two tool classes were proposed, namely usable and unsuitable, and tool corner wear VBc = 0.3 mm was assumed as a wear criterium. The diagnostic measures based on acceleration vibration signals were selected as input to the models. Additionally, the assessment of significant features in the division into usable and unsuitable class was caried out. Finally, this study evaluated chosen methods (classification and regression tree, induced fuzzy rules, and artificial neural network) and selected the most effective model.
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Launet, Laëtitia, Yuandou Wang, Adrián Colomer, Jorge Igual, Cristian Pulgarín-Ospina, Spiros Koulouzis, Riccardo Bianchi, et al. "Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions." Applied Sciences 13, no. 2 (January 9, 2023): 919. http://dx.doi.org/10.3390/app13020919.
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Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emerged as a way to train collaborative models among multiple institutions without having to share the raw data used for model training. However, although artificial intelligence experts have the expertise to develop state-of-the-art models and actively share their code through notebook environments, implementing a federated learning system in real-world applications entails significant engineering and deployment efforts. To reduce the complexity of federation setups and bridge the gap between federated learning and notebook users, this paper introduces a solution that leverages the Jupyter environment as part of the federated learning pipeline and simplifies its automation, the Notebook Federator. The feasibility of this approach is then demonstrated with a collaborative model solving a digital pathology image analysis task in which the federated model reaches an accuracy of 0.8633 on the test set, as compared to the centralized configurations for each institution obtaining 0.7881, 0.6514, and 0.8096, respectively. As a fast and reproducible tool, the proposed solution enables the deployment of a cross-country federated environment in only a few minutes.
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Parekh, Nisha Harish, and Mrs Vrushali Shinde. "Federated Learning : A Paradigm Shift in Collaborative Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (November 10, 2024): 1–6. http://dx.doi.org/10.55041/ijsrem38501.
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Federated learning (FL) has emerged as an exceptionally promising method within the realm of machine learning, enabling multiple entities to jointly train a global model while maintaining decentralized data. This paper presents a comprehensive review of federated learning methodologies, applications, and challenges. We begin by elucidating the fundamental concepts underlying FL, including federated optimization algorithms, communication protocols, and privacy-preserving techniques. Subsequently, we delve into various domains where FL has found significant traction, examples include healthcare, finance, and the Internet of Things (IoT), showcasing successful deployments and innovative strategies. Furthermore, we discuss the inherent challenges associated with federated learning, such as communication overhead, heterogeneity of data sources, and privacy concerns, and explore state- of-the-art solutions proposed in literature. Finally, we outline future research directions in federated learning, including advancements in privacy-preserving techniques, scalability improvements, and extension of FL to emerging domains. This thorough examination provides a valuable asset for researchers, practitioners, and policymakers keen on grasping the panorama of federated learning and its ramifications for collaborative machine learning in dispersed settings.
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Шубин, Б., Т. Максимюк, О. Яремко, Л. Фабрі, and Д. Мрозек. "МОДЕЛЬ ІНТЕГРАЦІЇ ФЕДЕРАТИВНОГО НАВЧАННЯ В МЕРЕЖІ МОБІЛЬНОГО ЗВ’ЯЗКУ 5-ГО ПОКОЛІННЯ." Information and communication technologies, electronic engineering 2, no. 1 (August 2022): 26–35. http://dx.doi.org/10.23939/ictee2022.01.026.
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This paper investigates the main advantages of using Federated Learning (FL) for sharing experiences between intelligent devices in the environment of 5th generation mobile communication networks. This approach makes it possible to build effective machine learning algorithms using confidential data, the loss of which may be undesirable or even dangerous for users. Therefore, for the tasks where the confidentiality of the data is required for processing and analysis, we suggest using Federated Learning (FL) approaches. In this case, all users' personal information will be processed locally on their devices. FL ensures the security of confidential data for subscribers, allows mobile network operators to reduce the amount of redundant information in the radio channel, and also allows optimizing the functioning of the mobile network. The paper presents a three-level model of integration of Federated Learning into the mobile network and describes the main features of this approach, as well as experimental studies that demonstrate the results of the proposed approach.
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Li, Chengan. "Research advanced in the integration of federated learning and reinforcement learning." Applied and Computational Engineering 40, no. 1 (February 21, 2024): 147–54. http://dx.doi.org/10.54254/2755-2721/40/20230641.
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Reinforcement learning (RL) and federated learning (FL) are two important machine learning paradigms. Reinforcement learning is concerned with enabling intelligence to learn optimal policies when interacting with an environment, while federated learning is concerned with collaboratively training models on distributed equipment while preserving data privacy. In recent years, the fusion and complementarity of reinforcement learning, and federated learning have attracted increasing research interest, providing new directions for the development of the machine learning community. Focusing on the integration of reinforcement learning and federated learning, this paper introduces in detail the latest technological developments in the integration of reinforcement learning and federated learning, and discusses the main challenges, existing methods and future directions of this intersection. Specifically, based on the introduction of classical reinforcement learning and federated learning. In addition, this document introduces cutting-edge results on the integration of reinforcement learning and joint learning and discusses the problems and future directions of the integration.
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K. Usha Rani, Sreenivasulu Reddy L., Yaswanth Kumar Alapati, M. Katyayani, Kumar Keshamoni, A. Sree Rama Chandra Murthy,. ""Federated Learning: Advancements, Applications, and Future Directions for Collaborative Machine Learning in Distributed Environments"." Journal of Electrical Systems 20, no. 5s (April 13, 2024): 165–71. http://dx.doi.org/10.52783/jes.1900.
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Federated Learning (FL) has become widely recognized as a feasible method for training machine learning models on decentralized devices, ensuring the preservation of data privacy. This study offers an extensive overview of the latest progress in federated learning methods, their applications, and the challenges they entail. We begin by introducing the concept of federated learning and its significance in distributed environments. Next, we delve into a range of methodologies aimed at improving the effectiveness, scalability, and confidentiality of federated learning. These encompass optimization algorithms, communication protocols, and mechanisms designed to uphold privacy. Moreover, we investigate the broad spectrum of applications where federated learning finds utility, spanning healthcare, the Internet of Things (IoT), and edge computing. This exploration illuminates tangible scenarios and advantages in real-world settings. Top of Form
Additionally, we analyze the challenges and limitations inherent in federated learning, including communication overhead, non-IID data distribution, and model heterogeneity. We review recent research efforts aimed at addressing these challenges, such as federated averaging variants, adaptive client selection, and robust aggregation techniques. Finally, we outline future research directions and potential avenues for the advancement of federated learning, emphasizing the need for standardized benchmarks, federated learning frameworks, and interdisciplinary collaborations.
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Delfin, Carl, Iulian Dragan, Dmitry Kuznetsov, Juan Fernandez Tajes, Femke Smit, Daniel E. Coral, Ali Farzaneh, et al. "A Federated Database for Obesity Research: An IMI-SOPHIA Study." Life 14, no. 2 (February 16, 2024): 262. http://dx.doi.org/10.3390/life14020262.
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Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.
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Seol, Mihye, and Taejoon Kim. "Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data." Sensors 23, no. 3 (January 19, 2023): 1152. http://dx.doi.org/10.3390/s23031152.
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Due to the distributed data collection and learning in federated learnings, many clients conduct local training with non-independent and identically distributed (non-IID) datasets. Accordingly, the training from these datasets results in severe performance degradation. We propose an efficient algorithm for enhancing the performance of federated learning by overcoming the negative effects of non-IID datasets. First, the intra-client class imbalance is reduced by rendering the class distribution of clients close to Uniform distribution. Second, the clients to participate in federated learning are selected to make their integrated class distribution close to Uniform distribution for the purpose of mitigating the inter-client class imbalance, which represents the class distribution difference among clients. In addition, the amount of local training data for the selected clients is finely adjusted. Finally, in order to increase the efficiency of federated learning, the batch size and the learning rate of local training for the selected clients are dynamically controlled reflecting the effective size of the local dataset for each client. In the performance evaluation on CIFAR-10 and MNIST datasets, the proposed algorithm achieves 20% higher accuracy than existing federated learning algorithms. Moreover, in achieving this huge accuracy improvement, the proposed algorithm uses less computation and communication resources compared to existing algorithms in terms of the amount of data used and the number of clients joined in the training.
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Zhang, Yong, and Mingchuan Zhang. "A Survey of Developments in Federated Meta-Learning." Academic Journal of Science and Technology 11, no. 2 (June 12, 2024): 27–29. http://dx.doi.org/10.54097/bzpfwa11.
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Federated meta-learning is a widely used few-shot learning method and has a very good development prospect. Federated meta-learning combines the characteristics of federated learning and meta-learning. It can not only use the data of each client while protecting its privacy to a certain extent, but also solve the problem of data volume that requires a large amount of data for model training in machine learning. With the rise of big data technology and edge computing, federated meta-learning technology has become a research hotspot in machine learning. In this paper, we provide an overview of the development of federated meta-learning and point out the relationship between federated learning, meta-learning and federated learning. Finally, some existing problems in federated meta-learning are pointed out, which provides ideas for the subsequent research on federated meta-learning.
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Raju Cherukuri, Bangar. "Federated Learning: Privacy-Preserving Machine Learning in Cloud Environments." International Journal of Science and Research (IJSR) 13, no. 10 (October 5, 2024): 1539–49. http://dx.doi.org/10.21275/ms241022095645.
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Jinhyeok Jang, Jinhyeok Jang, Yoonju Oh Jinhyeok Jang, Gwonsang Ryu Yoonju Oh, and Daeseon Choi Gwonsang Ryu. "Data Reconstruction Attack with Label Guessing for Federated Learning." 網際網路技術學刊 24, no. 4 (July 2023): 893–903. http://dx.doi.org/10.53106/160792642023072404007.
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<p>In light of recent advancements in deep and machine learning, federated learning has been proposed as a means to prevent privacy invasion. However, a reconstruction attack that exploits gradients to leak learning data has recently been developed. With increasing research into federated learning and the importance of data usage, it is crucial to prepare for such attacks. Specifically, when face data are used in federated learning, the damage caused by privacy infringement can be significant. Therefore, attack studies are necessary to develop effective defense strategies against these attacks. In this study, we propose a new attack method that uses labels to achieve faster and more accurate reconstruction performance than previous reconstruction attacks. We demonstrate the effectiveness of our proposed method on the Yale Face Database B, MNIST, and CIFAR-10 datasets, as well as under non-IID conditions, similar to real federated learning. The results show that our proposed method outperforms random labeling in terms of reconstruction performance in all evaluations for MNIST and CIFAR-10 datasets in round 1.</p> <p> </p>
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Alaa Hamza Omran, Sahar Yousif Mohammed, and Mohammad Aljanabi. "Detecting Data Poisoning Attacks in Federated Learning for Healthcare Applications Using Deep Learning." Iraqi Journal For Computer Science and Mathematics 4, no. 4 (November 26, 2023): 225–37. http://dx.doi.org/10.52866/ijcsm.2023.04.04.018.
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This work presents a novel method for securing federated learning in healthcare applications, focusing on skin cancer classification. The suggested solution detects and mitigates data poisoning attacks using deep learning and CNN architecture, specifically VGG16. In a federated learning architecture with ten healthcare institutions, the approach ensures collaborative model training while protecting sensitive medical data. Data is meticulously prepared and preprocessed using the Skin Cancer MNIST: HAM10000 dataset. The federated learning approach uses VGG16's powerful feature extraction to classify skin cancer. A robust strategy for spotting data poisoning threats in federated learning is presented in the study. Outlier detection techniques and strict criteria flag andevaluate problematic model modifications. Performance evaluation proves the model's accuracy, privacy, and datapoisoning resilience. This research presents federated learning-based skin cancer categorization for healthcareapplications that is secure and accurate. The suggested approach improves healthcare diagnostics and emphasizesdata security and privacy in federated learning settings by tackling data poisoning attacks.
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Guo, Wenxin. "Overview of Research Progress and Challenges in Federated Learning." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 797–804. http://dx.doi.org/10.62051/9qyaha16.
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In recent years, federated learning has become a hot research topic in the machine learning community. It aims to reduce the potential data security and privacy risks caused by the centralized training paradigm of traditional machine learning through local training and global aggregation. Although federated learning methods have been widely applied in numerous fields such as finance, healthcare, autonomous driving, and smart retail, there are still urgent issues to be addressed in the field of federated learning, including data privacy leakage, malicious node attacks, model security, and the trustworthiness of participants. By delving into and discussing federated learning, this paper aims to provide researchers and practitioners in related fields with a comprehensive understanding and the latest progress of this technology. Based on the characteristics of the data distribution of the parties involved in federated learning training, this paper categorizes existing federated learning methods into horizontal federated learning, vertical federated learning, and federated transfer learning. It also introduces representative federated learning algorithms under different types, including their design concepts, basic processes, and advantages and disadvantages. Combining different application scenarios, this paper further discusses the challenges of federated learning and looks forward to the future development direction of this topic.
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Monteiro, Daryn, Ishaan Mavinkurve, Parth Kambli, and Prof Sakshi Surve. "Federated Learning for Privacy-Preserving Machine Learning: Decentralized Model Training with Enhanced Data Security." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (November 30, 2024): 355–61. http://dx.doi.org/10.22214/ijraset.2024.65062.
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Abstract: Artificial Intelligence has found widespread use across various industries, from optimizing manufacturing workflows to diagnosing health conditions. However, the large volumes of data required to train AI models raise privacy concerns, especially when stored in centralized databases vulnerable to leaks. Federated Learning solves this problem by training models collaboratively by avoiding centralization of the sensitive data, preserving privacy while allowing decentralized models to be exported to edge devices. This paper explores Federated Learning, focusing on its technical aspects, algorithms, and decentralized architecture. By keeping raw data localized, Federated Learning enables global models while safeguarding individual privacy, fostering collaboration across sectors like healthcare, finance, and IoT. It also addresses challenges such as privacy vulnerabilities and model aggregation across devices, proposing solutions to strengthen Federated Learning's effectiveness. Ultimately, this study highlights Federated Learning's pivotal role in the future of AI, where privacy preservation and collaboration are key. By balancing model performance with data privacy, Federated Learning stands as a promising framework for responsible and inclusive AI development.
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Alferaidi, Ali, Kusum Yadav, Yasser Alharbi, Wattana Viriyasitavat, Sandeep Kautish, and Gaurav Dhiman. "Federated Learning Algorithms to Optimize the Client and Cost Selections." Mathematical Problems in Engineering 2022 (April 1, 2022): 1–9. http://dx.doi.org/10.1155/2022/8514562.
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In recent years, federated learning has received widespread attention as a technology to solve the problem of data islands, and it has begun to be applied in fields such as finance, healthcare, and smart cities. The federated learning algorithm is systematically explained from three levels. First, federated learning is defined through the definition, architecture, classification of federated learning, and comparison with traditional distributed knowledge. Then, based on machine learning and deep learning, the current types of federated learning algorithms are classified, compared, and analyzed in-depth. Finally, the communication from the perspectives of cost, client selection, and aggregation method optimization, the federated learning optimization algorithms are classified. Finally, the current research status of federated learning is summarized. Finally, the three major problems and solutions of communication, system heterogeneity, and data heterogeneity faced by federated learning are proposed and expectations for the future.
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Feng, Zecheng. "Federated Learning Security Threats and Defense Approaches." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 121–27. http://dx.doi.org/10.54097/wvfhcd40.
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Artificial intelligence technology has developed rapidly. As a new technology, Federated learning can keep all parties' data locally and train the global model together with all data parties. Therefore, it can solve the problem of "data islands" while protecting privacy, so Federated learning is widely used. However, the existing Federated learning system still has many loopholes. For example, when uploading a local model, an attacker may mix in models with incorrect data. This requires corresponding defensive measures. Before beginning this article, we learned about the previous work related to the security threats and defense measures of Federated learning. This paper first explains the concept, advantages, and disadvantages of Federated learning. Secondly, it summarizes five common security threats in Federated learning and explains and compares various threats. Then it summarizes four defense approaches commonly used in Federated learning and explains each approach in principle. Finally, this paper looks forward to the follow-up development of defense methods in Federated learning.
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Ning, Weiguang, Yingjuan Zhu, Caixia Song, Hongxia Li, Lihui Zhu, Jinbao Xie, Tianyu Chen, Tong Xu, Xi Xu, and Jiwei Gao. "Blockchain-Based Federated Learning: A Survey and New Perspectives." Applied Sciences 14, no. 20 (October 16, 2024): 9459. http://dx.doi.org/10.3390/app14209459.
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Federated learning, as a novel distributed machine learning mode, enables the training of machine learning models on multiple devices while ensuring data privacy. However, the existence of single-point-of-failure bottlenecks, malicious threats, scalability of federated learning implementation, and lack of incentive mechanisms have seriously hindered the development of federated learning technology. In recent years, as a distributed ledger, blockchain has the characteristics of decentralization, tamper-proof, transparency, security, etc., which can solve the issues encountered in the above-mentioned federated learning. Particularly, the integration of federated learning and blockchain leads to a new paradigm, called blockchain-based federated learning (BFL), which has been successfully applied in many application scenarios. This paper aims to provide a comprehensive review of recent efforts on blockchain-based federated learning. More concretely, we propose and design a taxonomy of blockchain-based federated learning models, along with providing a comprehensive summary of the state of the art. Various applications of federated learning based on blockchain are introduced. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development in the field.
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Jitendra Singh Chouhan, Amit Kumar Bhatt, Nitin Anand. "Federated Learning; Privacy Preserving Machine Learning for Decentralized Data." Tuijin Jishu/Journal of Propulsion Technology 44, no. 1 (November 24, 2023): 167–69. http://dx.doi.org/10.52783/tjjpt.v44.i1.2234.
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Federated learning represents a compelling solution for tackling the privacy challenges inherent in decentralized and distributed environments when it comes to machine learning. This scholarly paper delves deep into the realm of federated learning, encompassing its applications and the latest privacy-preserving techniques used for training machine learning models in a decentralized manner. We explore the reasons behind the adoption of federated learning, highlight its advantages over conventional centralized approaches, and examine the diverse methods employed to safeguard privacy within this framework. Furthermore, we scrutinize the current obstacles, unresolved research queries, and the prospective directions within this rapidly developing field
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Monika Dhananjay Rokade. "Advancements in Privacy-Preserving Techniques for Federated Learning: A Machine Learning Perspective." Journal of Electrical Systems 20, no. 2s (March 31, 2024): 1075–88. http://dx.doi.org/10.52783/jes.1754.
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Federated learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. However, concerns about data privacy remain significant, particularly in scenarios where sensitive information is involved. This paper reviews recent advancements in privacy-preserving techniques for federated learning from a machine learning perspective. It categorizes and analyses state-of-the-art approaches within a unified framework, highlighting their strengths, limitations, and potential applications. By providing insights into the landscape of privacy-preserving federated learning, this review aims to guide researchers and practitioners in developing robust and privacy-conscious machine learning solutions for collaborative environments. The paper concludes with future research directions to address ongoing challenges and further enhance the effectiveness and scalability of privacy-preserving federated learning.
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Liu, Chaoyi, and Qi Zhu. "Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning." Applied Sciences 13, no. 6 (March 15, 2023): 3771. http://dx.doi.org/10.3390/app13063771.
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Aiming at the unmanned aerial vehicle (UAV)-assisted federated learning wireless-network scenario, and considering the influence of the UAV altitude on the coverage area, we propose a joint optimization algorithm of UAV placement, computation and communication resources. Considering the energy efficiency and federated learning performance, we defined the cost function of the system. Under the constraint of the total delay of federated learning completion, we formulated an optimization problem of minimizing the cost function to achieve the balance between the total energy consumption of users and the federated learning performance. Since the formulated optimization problem is a non-convex problem, in order to solve this problem, we decomposed it into three optimization subproblems: UAV horizontal placement, local accuracy and computation and communication resources. We used successive convex approximation (SCA), the Dinkelbach Method, the Bisection method and KKT condition, respectively, to solve the three subproblems, and finally obtain the optimal solutions through iteration of the three subproblems. Simulation results show that compared with the federated learning scenario under fixed UAV altitude, our proposed algorithm not only guarantees the learning performance, but also reduces more users’ total energy consumption.
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Yarlagadda, Sneha Sree, Sai Harshith Tule, and Karthik Myada. "F1 Score Based Weighted Asynchronous Federated Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (February 29, 2024): 947–53. http://dx.doi.org/10.22214/ijraset.2024.58487.
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Abstract: The domain of federated learning has observed remarkable developments in recent years, enabling collaborative model training while preserving data privacy. This paper discusses several recent advancements in the field of federated learning, particularly in asynchronous and weighted federated learning. A novel approach within the federated learning paradigm titled "F1 Score Based Weighted Asynchronous Federated Learning" is introduced. The approach addresses issues of biased aggregation and device heterogeneity by assigning weights to devices based on their F1 scores, prioritizing those with superior performance in classification. Leveraging the asynchronous nature of federated learning, this approach enhances both resource efficiency and convergence speed. By incorporating F1 scores into the weighting mechanism, a balanced emphasis on precision and recall is achieved. It can optimize resource utilization, expediting the learning process by focusing on updates from devices with more accurate and valuable information. This approach can enhance collaborative model improvement while preserving data privacy in federated learning.
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Liu, Jessica Chia, Jack Goetz, Srijan Sen, and Ambuj Tewari. "Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data." JMIR mHealth and uHealth 9, no. 3 (March 30, 2021): e23728. http://dx.doi.org/10.2196/23728.
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Background The use of wearables facilitates data collection at a previously unobtainable scale, enabling the construction of complex predictive models with the potential to improve health. However, the highly personal nature of these data requires strong privacy protection against data breaches and the use of data in a way that users do not intend. One method to protect user privacy while taking advantage of sharing data across users is federated learning, a technique that allows a machine learning model to be trained using data from all users while only storing a user’s data on that user’s device. By keeping data on users’ devices, federated learning protects users’ private data from data leaks and breaches on the researcher’s central server and provides users with more control over how and when their data are used. However, there are few rigorous studies on the effectiveness of federated learning in the mobile health (mHealth) domain. Objective We review federated learning and assess whether it can be useful in the mHealth field, especially for addressing common mHealth challenges such as privacy concerns and user heterogeneity. The aims of this study are to describe federated learning in an mHealth context, apply a simulation of federated learning to an mHealth data set, and compare the performance of federated learning with the performance of other predictive models. Methods We applied a simulation of federated learning to predict the affective state of 15 subjects using physiological and motion data collected from a chest-worn device for approximately 36 minutes. We compared the results from this federated model with those from a centralized or server model and with the results from training individual models for each subject. Results In a 3-class classification problem using physiological and motion data to predict whether the subject was undertaking a neutral, amusing, or stressful task, the federated model achieved 92.8% accuracy on average, the server model achieved 93.2% accuracy on average, and the individual model achieved 90.2% accuracy on average. Conclusions Our findings support the potential for using federated learning in mHealth. The results showed that the federated model performed better than a model trained separately on each individual and nearly as well as the server model. As federated learning offers more privacy than a server model, it may be a valuable option for designing sensitive data collection methods.
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Toofanee, Mohammud Shaad Ally, Mohamed Hamroun, Sabeena Dowlut, Karim Tamine, Vincent Petit, Anh Kiet Duong, and Damien Sauveron. "Federated Learning: Centralized and P2P for a Siamese Deep Learning Model for Diabetes Foot Ulcer Classification." Applied Sciences 13, no. 23 (November 28, 2023): 12776. http://dx.doi.org/10.3390/app132312776.
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It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly among healthcare institutions, the need to maintain the confidentiality of sensitive information often restricts the comprehensive utilization of real-world data in machine learning. To address this challenge, our study experiments with an innovative approach using federated learning to enable collaborative model training without compromising data confidentiality and privacy. We present an adaptation of the federated averaging algorithm, a predominant centralized learning algorithm, to a peer-to-peer federated learning environment. This adaptation led to the development of two extended algorithms: Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer. These algorithms were applied to train deep neural network models for the detection and monitoring of diabetic foot ulcers, a critical health condition among diabetic patients. This study compares the performance of Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer with their centralized counterparts in terms of model convergence and communication costs. Additionally, we explore enhancements to these algorithms using targeted heuristics based on client identities and f1-scores for each class. The results indicate that models utilizing peer-to-peer federated averaging achieve a level of convergence that is comparable to that of models trained via conventional centralized federated learning approaches. This represents a notable progression in the field of ensuring the confidentiality and privacy of medical data for training machine learning models.
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Li, Jipeng, Xinyi Li, and Chenjing Zhang. "Analysis on Security and Privacy-preserving in Federated Learning." Highlights in Science, Engineering and Technology 4 (July 26, 2022): 349–58. http://dx.doi.org/10.54097/hset.v4i.923.
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Data privacy breaches during the training and implementation of the model are the main challenges that impede the development of artificial intelligence technologies today. Federated Learning has been an effective tool for the protection of privacy. Federated Learning is a distributive machine learning method that trains a non-destructive learning module based on a local training and passage of parameters from participants, with no required direct access to data source. Federated Learning still holds many pitfalls. This paper first introduces the types of federated learning, including horizontal federated learning, vertical federated learning and federated transfer learning, and then analyses the existing security risks of poisoning attacks, adversarial attacks and privacy leaks, with privacy leaks becoming a security risk that cannot be ignored at this stage. This paper also summarizes the corresponding defence measures, from three aspects: Poison attack defence, Privacy Leak Defence, and Defence against attack, respectively. This paper introduces the defence measures taken against some threats faced by federated learning, and finally gives some future research directions.
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Woo, Gimoon, Hyungbin Kim, Seunghyun Park, Cheolwoo You, and Hyunhee Park. "Fairness-Based Multi-AP Coordination Using Federated Learning in Wi-Fi 7." Sensors 22, no. 24 (December 13, 2022): 9776. http://dx.doi.org/10.3390/s22249776.
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Federated learning is a type of distributed machine learning in which models learn by using large-scale decentralized data between servers and devices. In a short-range wireless communication environment, it can be difficult to apply federated learning because the number of devices in one access point (AP) is small, which can be small enough to perform federated learning. Therefore, it means that the minimum number of devices required to perform federated learning cannot be matched by the devices included in one AP environment. To do this, we propose to obtain a uniform global model regardless of data distribution by considering the multi-AP coordination characteristics of IEEE 802.11be in a decentralized federated learning environment. The proposed method can solve the imbalance in data transmission due to the non-independent and identically distributed (non-IID) environment in a decentralized federated learning environment. In addition, we can also ensure the fairness of multi-APs and determine the update criteria for newly elected primary-APs by considering the learning training time of multi-APs and energy consumption of grouped devices performing federated learning. Thus, our proposed method can determine the primary-AP according to the number of devices participating in the federated learning in each AP during the initial federated learning to consider the communication efficiency. After the initial federated learning, fairness can be guaranteed by determining the primary-AP through the training time of each AP. As a result of performing decentralized federated learning using the MNIST and FMNIST dataset, the proposed method showed up to a 97.6% prediction accuracy. In other words, it can be seen that, even in a non-IID multi-AP environment, the update of the global model for federated learning is performed fairly.
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DR.AR.SIVAKUMARAN, POLNENI ABHINAYA, PENDYALA SWETHA, and POKALA MAHITHA. "DATA POISONING ATTACKS ON FEDERATED MACHINE LEARNING." International Journal of Engineering, Science and Advanced Technology 24, no. 10 (2024): 188–97. http://dx.doi.org/10.36893/ijesat.2024.v24i10.022.
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Federated machine learning which enables resource constrained node devices (e.g., mobile phones and IoT devices) to learn a shared model while keeping the training data local, can provide privacy, security and economic benefits by designing an effective communication protocol. However, the communication protocol amongst different nodes could be exploited by attackers to launch data poisoning attacks, which has been demonstrated as a big threat to most machine learning models. In this paper, we attempt to explore the vulnerability of federated machine learning. More specifically, we focus on attacking a federated multi-task learning framework, which is a federated learning framework via adopting a general multi-task learning framework to handle statistical challenges. We formulate the problem of computing optimal poisoning attacks on federated multitask learning as a bilevel program that is adaptive to arbitrary choice of target nodes and source attacking nodes. Then we propose a novel systems-aware optimization method, ATTack on Federated Learning (AT2FL), which is efficiency to derive the implicit gradients for poisoned data, and further compute optimal attack strategies in the federated machine learning. Our work is an earlier study that considers issues of data poisoning attack for federated learning. To the end, experimental results on real-world datasets show that federated multi-task learning model is very sensitive to poisoning attacks, when the attackers either directly poison the target nodes or indirectly poison the related nodes by exploiting the communication protocol.
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Chouhan, Khushi Udaysingh, Nikita Pradeep Kumar Jha, Roshni Sanjay Jha, Shaikh Insha Kamaluddin, and Dr Nupur Giri. "Mobile Keyword Prediction using Federated Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 3144–51. http://dx.doi.org/10.22214/ijraset.2023.50826.
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Abstract: Federated learning is a decentralized form of Machine Learning in which data subsets are trained on several edge devices aggregated and brought to the centralized server. Here, federated applications store the local copy on all the edge devices such as smartphones where users can use it accordingly. The model gradually learns and trains itself from inputs by the user’s virtual keyboard and becomes smarter iteratively. Devices transfer the results in the form of parameters to the centralized server where these results are aggregated with the help of federated algorithms. For the purpose of next word prediction using federated learning, we implemented different algorithms such as FedAvg, FedProx, FedSgd. In reference to the traditional ML models, data is collected in a centralized location and is used to train the model. However, unlike ML techniques the Federated Learning techniques give more control over user’s data to themselves since the data is not shared with the central server or other devices. Thus, federated learning adheres to the data privacy and feasibility for its users
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Wang, Weixi. "Empowering safe and secure autonomy: Federated learning in the era of autonomous driving." Applied and Computational Engineering 51, no. 1 (March 25, 2024): 40–44. http://dx.doi.org/10.54254/2755-2721/51/20241158.
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Artificial Intelligence (AI) has a significant impact on empowering autonomous driving systems to perceive and interpret the environment effectively. However, ensuring data privacy and security in autonomous driving systems is a critical challenge. To surmount these hurdles, federated learning has emerged as an effective strategy. Federated learning is a decentralized machine learning approach that facilitates the cooperative training of models across a diverse set of connected devices, enabling them to collectively learn and improve their performance, while preserving data privacy. This approach eliminates the necessity of sharing raw data and only involves sharing model updates with a central aggregator, thereby ensuring privacy and minimizing data exposure. This paper examines the implementation of federated learning in autonomous driving. It explores the principles of federated learning, including decentralized training, local model updates, model aggregation, privacy preservation, iterative learning, and heterogeneity handling. Two specific approaches, Deep Federated Learning (DFL) and End-to-End Federated Learning, are discussed, highlighting their benefits in enhancing privacy and maintaining prediction accuracy. The paper also discusses the applications of federated learning in communication and control aspects of autonomous driving. It emphasizes the scalability, adaptability, edge computing, real-time learning, federated transfer learning, and privacy-preserving data sharing as potential future prospects for federated learning in autonomous driving. Overall, federated learning offers a unique opportunity to address privacy concerns in autonomous driving systems while harnessing the collective intelligence of a fleet of vehicles. It has the potential to revolutionize the field and contribute to the development of safe and secure autonomous driving technologies.
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Ajay, Ajay, Ajay Kumar, Krishan Kant Singh Gautam, Pratibha Deshmukh, Pavithra G, and Laith Abualigah. "Collaborative Intelligence for IoT: Decentralized Net security and confidentiality." Journal of Intelligent Systems and Internet of Things 13, no. 2 (2024): 202–11. http://dx.doi.org/10.54216/jisiot.130216.
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This research compares federated and centralized learning paradigms to discover the best machine learning privacy-model accuracy balance. Federated learning allows model training across devices or clients without data centralization. It's innovative distributed machine learning. Keeping data on individual devices reduces the hazards of centralized data storage, improving user privacy and security. However, centralized learning concentrates data on a server, which raises privacy and security problems. It evaluates two learning approaches using simulated data in a simple regression problem framework. Federated learning seems to be as accurate as centralized learning while protecting privacy. The paper also shows how federated learning works in popular machine learning frameworks like TensorFlow Federated. This research shows that federated learning protects privacy while producing accurate machine learning models. It challenges the idea that machine learning must constantly choose between privacy and accuracy. Empirical facts and theoretical ideas from this study advance machine learning methodology discussions. In the digital era, it promotes privacy-conscious, dispersed learning frameworks.
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Emmanni, Phani Sekhar. "Federated Learning for Cybersecurity in Edge and Cloud Computing." International Journal of Computing and Engineering 5, no. 4 (March 12, 2024): 27–38. http://dx.doi.org/10.47941/ijce.1829.
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Purpose: The article explores the integration of federated learning within edge and cloud computing frameworks to address complex cybersecurity challenges. It aims to illustrate how federated learning, by enabling collaborative model training across decentralized devices without data exchange, can serve as an effective mechanism for enhancing cybersecurity defenses. This study investigates the potential of federated learning to improve privacy-preserving data analysis and augment real-time threat detection capabilities in the context of the growing Internet of Things (IoT) ecosystem. Methodology: The research delves into the conceptual framework of federated learning, examining its application in cybersecurity contexts through a detailed literature review and theoretical analysis. It evaluates the benefits and limitations of federated learning in enhancing data privacy and reducing latency in threat detection. Furthermore, the article assesses the technical and security challenges of implementing federated learning, including communication overhead, model aggregation complexities, and vulnerability to model poisoning, through qualitative analysis. Findings: The study finds that federated learning significantly improves privacy-preserving data analysis and enhances real-time threat detection capabilities by keeping data localized while enabling collaborative learning. However, it also identifies key challenges in deploying federated learning strategies, such as the risk of model poisoning and the complexities involved in model aggregation and communication overhead. The research highlights the need for robust mechanisms to address these challenges to fully leverage federated learning in cybersecurity. Unique Contribution to Theory, Policy, and Practice: This article contributes uniquely to the theoretical understanding of federated learning as a cybersecurity measure, offering a comprehensive analysis of its applications, benefits, and limitations within edge and cloud computing environments. Practically, it provides insights for cybersecurity professionals and researchers on integrating federated learning into existing cybersecurity frameworks to enhance data privacy and threat detection. The article recommends further exploration into combining federated learning with other cutting-edge technologies to develop resilient cybersecurity measures. Additionally, it suggests that policymakers should consider the implications of federated learning on data privacy regulations and cybersecurity standards. Through its thorough examination of federated learning's potential and challenges, the article offers valuable recommendations for fortifying cybersecurity frameworks in an increasingly interconnected world.
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Zhang, Ticao, and Shiwen Mao. "An Introduction to the Federated Learning Standard." GetMobile: Mobile Computing and Communications 25, no. 3 (January 7, 2022): 18–22. http://dx.doi.org/10.1145/3511285.3511291.
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With the growing concern on data privacy and security, it is undesirable to collect data from all users to perform machine learning tasks. Federated learning, a decentralized learning framework, was proposed to construct a shared prediction model while keeping owners' data on their own devices. This paper presents an introduction to the emerging federated learning standard and discusses its various aspects, including i) an overview of federated learning, ii) types of federated learning, iii) major concerns and the performance evaluation criteria of federated learning, and iv) associated regulatory requirements. The purpose of this paper is to provide an understanding of the standard and facilitate its usage in model building across organizations while meeting privacy and security concerns.
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Lee, Haeyun, Young Jun Chai, Hyunjin Joo, Kyungsu Lee, Jae Youn Hwang, Seok-Mo Kim, Kwangsoon Kim, et al. "Federated Learning for Thyroid Ultrasound Image Analysis to Protect Personal Information: Validation Study in a Real Health Care Environment." JMIR Medical Informatics 9, no. 5 (May 18, 2021): e25869. http://dx.doi.org/10.2196/25869.
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Background Federated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms. Federated learning mitigates many systemic privacy risks by sharing only the model and parameters for training, without the need to export existing medical data sets. In this study, we performed ultrasound image analysis using federated learning to predict whether thyroid nodules were benign or malignant. Objective The goal of this study was to evaluate whether the performance of federated learning was comparable with that of conventional deep learning. Methods A total of 8457 (5375 malignant, 3082 benign) ultrasound images were collected from 6 institutions and used for federated learning and conventional deep learning. Five deep learning networks (VGG19, ResNet50, ResNext50, SE-ResNet50, and SE-ResNext50) were used. Using stratified random sampling, we selected 20% (1075 malignant, 616 benign) of the total images for internal validation. For external validation, we used 100 ultrasound images (50 malignant, 50 benign) from another institution. Results For internal validation, the area under the receiver operating characteristic (AUROC) curve for federated learning was between 78.88% and 87.56%, and the AUROC for conventional deep learning was between 82.61% and 91.57%. For external validation, the AUROC for federated learning was between 75.20% and 86.72%, and the AUROC curve for conventional deep learning was between 73.04% and 91.04%. Conclusions We demonstrated that the performance of federated learning using decentralized data was comparable to that of conventional deep learning using pooled data. Federated learning might be potentially useful for analyzing medical images while protecting patients’ personal information.
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Tian, Junfeng, Xinyao Chen, and Shuo Wang. "Few-Shot Federated Learning: A Federated Learning Model for Small-Sample Scenarios." Applied Sciences 14, no. 9 (May 4, 2024): 3919. http://dx.doi.org/10.3390/app14093919.
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Traditional federated learning relies heavily on mature datasets, which typically consist of large volumes of uniformly distributed data. While acquiring extensive datasets is relatively straightforward in academic research, it becomes prohibitively expensive in practical applications, especially in emerging or specialized medical fields characterized by data scarcity. This poses a significant challenge. To address this issue, our study introduces a federated learning model that integrates few-shot learning techniques and is complemented by personalized knowledge distillation to further enhance the model’s classification accuracy. This innovative approach significantly reduces the dependence on large-scale datasets, enabling efficient model training under limited data conditions. Our experimental evaluations conducted on small-scale datasets, including Omniglot, FC100, and mini-ImageNet, indicate that our model surpasses existing state-of-the-art federated learning models in terms of accuracy, achieving a substantial improvement. Specifically, on the FC100 dataset, the classification accuracy of the conventional federated learning algorithm FedAvg was merely 19.6%, whereas the method proposed in this study achieved a classification accuracy of 41%, representing an improvement of more than double. This advancement not only highlights our model’s superiority in alleviating the challenges of limited data availability, but also expands the applicability of federated learning to a broader range of applications.
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Hang, Yifei. "Federated learning-based neural network for hotel cancellation prediction." Applied and Computational Engineering 45, no. 1 (March 15, 2024): 190–95. http://dx.doi.org/10.54254/2755-2721/45/20241092.
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Hotel reservations have become a prevalent choice for customers. However, cancellations of these reservations present a significant challenge for hotels, potentially resulting in financial losses and a decline in customer satisfaction. To address the issue of improper management of cancellations and minimize losses, machine learning can be employed to analyze and predict cancellations based on customer information. In cooperative scenarios where hotels collaborate to train a unified model, traditional algorithms that aggregate all data raise concerns about the protection of sensitive customer information. In this context, federated learning emerges as an effective solution to ensure the privacy protection of customers while achieving the desired predictive outcomes. Thus, to protect customer privacy while preserving performance of the federated learning model in comparison to the non-federated version, this work proposed to implement both federated learning and non-federated algorithms based on neural network to make predictions of cancellation based on multiple factors. The federated learning approach achieved a final testing accuracy of 76.64%. Although this accuracy was about 9% lower than the non-federated case, its loss was over half the loss of non-federated, and its testing accuracy was similar to the training accuracy, while the non-federated algorithms testing accuracy was approximately 4% lower than the training one. Such results indicate that although accuracy was relatively lower, the federated learning approach prevented the overfitting problem in the non-federated case, while the data privacy problem was resolved.
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Gao, Yuan. "Federated learning: Impact of different algorithms and models on prediction results based on fashion-MNIST data set." Applied and Computational Engineering 86, no. 1 (July 31, 2024): 210–18. http://dx.doi.org/10.54254/2755-2721/86/20241594.
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The realm of federated learning is rapidly advancing amid the era of big data. Therefore, how to select a suitable federated learning algorithm to achieve realistic tasks has become particularly critical. In this study, we explore the impact of different algorithms and models on the prediction results of Federated Learning (FL) using the Fashion-MNIST data set. Federated Learning enhances data privacy and reduces latency by training models directly on local devices since it is a decentralized machine learning approach. We analyze the performance of several FL algorithms including Federated Averaging (FedAvg), Federated Stochastic Gradient Descent (FedSGD), Federated Proximal (FedProx), and SCAFFOLD. Our experiments reveal significant differences in accuracy and stability among these algorithms, highlighting their strengths and weaknesses in handling non-IID (Non-Independent and Identically Distributed) data. FedProx demonstrate superior performance in terms of accuracy and robustness, making them suitable for complex federated learning environments. These discoveries offer crucial insights for choosing suitable FL algorithms and models in practical applications.
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Al-Tameemi, M., M. B. Hassan, and S. A. Abass. "Federated Learning (FL) – Overview." LETI Transactions on Electrical Engineering & Computer Science 17, no. 5 (2024): 74–82. http://dx.doi.org/10.32603/2071-8985-2024-17-5-74-82.
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Explores the fundamental aspects of federated learning (FL) in the context of intrusion detection systems (IDS) within Internet of Things (IoT) networks. Federated learning presents an innovative approach to training machine learning models on distributed devices, thereby minimizing the need to transmit sensitive data to central servers. We classify FL into horizontal, vertical, and federated transfer learning and examine their application in IDS systems. Additionally, we analyze the network structure of FL, encompassing centralized and decentralized FL. Based on the conducted review, it can be concluded that FL holds promise for enhancing data privacy and anomaly detection efficiency in IoT networks.
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Li, Sirui, Keyu Shao, and Jingqi Zhou. "Research Advanced in Federated Learning." Applied and Computational Engineering 40, no. 1 (February 21, 2024): 140–46. http://dx.doi.org/10.54254/2755-2721/40/20230640.
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With the vigorous development of big data, cloud computing and other fields, it has become a global trend to pay attention to data security and privacy. In order to protect their own data security and privacy, different groups are unwilling to contribute their own data information, making the problem of data islands gradually prominent, which seriously restricts the further development of data-driven artificial intelligence. In order to alleviate the above problems, federated learning has attracted more researchers' attention in recent years. Federated Learning is a collaborative decentralized privacy-preserving technique that makes local data available to multiple parties, which not only can the private data be effectively used to train the model but also the leakage of private data can be avoided. Federated learning has been widely used in practical fields such as the financial industry and the Internet of Things industry. This paper systematically introduces the results of research in the field of federated learning in recent years. Specifically, three structures of federated learning are first introduced, and the differences between these three structures are introduced. Then, the most used datasets in training and validation stage were introduced and the shortcoming of each method were introduced to help advanced understanding of FL. Finally, several unsolved problems were introduced and the future prospects in federated learning domain were proposed.
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Chen, Gaofeng, and Qingtao Wu. "A Review of Personalized Federated Reinforcement Learning." International Journal of Computer Science and Information Technology 3, no. 1 (June 15, 2024): 1–9. http://dx.doi.org/10.62051/ijcsit.v3n1.01.
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Reinforcement learning and federated learning both provide strong theoretical support for the study of artificial intelligence. In recent years, an emerging federated reinforcement learning paradigm has been proposed and widely studied and applied. However, in the federated reinforcement learning architecture, the environment, data type and device performance of different agents may be different, which is called heterogeneity. The existence of heterogeneity factors may lead to slow convergence speed of the algorithm, poor generalization, and suboptimal quality of the trained model. Therefore, how to solve the negative impact of the heterogeneity problem on model training has become a hot content of research, and the most important method is to train personalized models for clients. This paper introduces the theory of federated reinforcement learning, as well as methods to cope with heterogeneity in federated reinforcement learning, and provides an overview of the applications of federated reinforcement learning. We conclude the paper with a summary and future perspectives.
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Jiang, Jingyan, Liang Hu, Chenghao Hu, Jiate Liu, and Zhi Wang. "BACombo—Bandwidth-Aware Decentralized Federated Learning." Electronics 9, no. 3 (March 5, 2020): 440. http://dx.doi.org/10.3390/electronics9030440.
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The emerging concern about data privacy and security has motivated the proposal of federated learning. Federated learning allows computing nodes to only synchronize the locally- trained models instead of their original data in distributed training. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized typologies and large nodes-to-server bandwidths. However, in real-world federated learning scenarios, the network capacities between nodes are highly uniformly distributed and smaller than that in data centers. As a result, how to efficiently utilize network capacities between computing nodes is crucial for conventional federated learning. In this paper, we propose Bandwidth Aware Combo (BACombo), a model segment level decentralized federated learning, to tackle this problem. In BACombo, we propose a segmented gossip aggregation mechanism that makes full use of node-to-node bandwidth for speeding up the communication time. Besides, a bandwidth-aware worker selection model further reduces the transmission delay by greedily choosing the bandwidth-sufficient worker. The convergence guarantees are provided for BACombo. The experimental results on various datasets demonstrate that the training time is reduced by up to 18 times that of baselines without accuracy degrade.
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Shrivastava, Arpit. "Privacy-Centric AI: Navigating the Landscape with Federated Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 357–63. http://dx.doi.org/10.22214/ijraset.2024.61000.
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Abstract: In the era of big data and privacy concerns, federated learning has emerged as a promising approach to training machine learning models while preserving data privacy. This paper explores the principles and applications of federated learning, highlighting its potential to revolutionize privacy-centric AI. We discuss the methodology, significance, and challenges of federated learning, providing insights into its future directions. By leveraging decentralized data and aggregating model updates, federated learning enables the development of powerful AI models without compromising individual privacy. We present real-world applications and cite relevant studies to demonstrate the transformative impact of federated learning across various domains.
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Jin, Xuan, Yuanzhi Yao, and Nenghai Yu. "Efficient secure aggregation for privacy-preserving federated learning based on secret sharing." JUSTC 53, no. 4 (2023): 1. http://dx.doi.org/10.52396/justc-2022-0116.
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Federated learning allows multiple mobile participants to jointly train a global model without revealing their local private data. Communication-computation cost and privacy preservation are key fundamental issues in federated learning. Existing secret sharing-based secure aggregation mechanisms for federated learning still suffer from significant additional costs, insufficient privacy preservation, and vulnerability to participant dropouts. In this paper, we aim to solve these issues by introducing flexible and effective secret sharing mechanisms into federated learning. We propose two novel privacy-preserving federated learning schemes: federated learning based on one-way secret sharing (FLOSS) and federated learning based on multishot secret sharing (FLMSS). Compared with the state-of-the-art works, FLOSS enables high privacy preservation while significantly reducing the communication cost by dynamically designing secretly shared content and objects. Meanwhile, FLMSS further reduces the additional cost and has the ability to efficiently enhance the robustness of participant dropouts in federated learning. Foremost, FLMSS achieves a satisfactory tradeoff between privacy preservation and communication-computation cost. Security analysis and performance evaluations on real datasets demonstrate the superiority of our proposed schemes in terms of model accuracy, privacy preservation, and cost reduction.
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Tian, Mengmeng. "An Contract Theory based Federated Learning Aggregation Algorithm in IoT Network." Journal of Physics: Conference Series 2258, no. 1 (April 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2258/1/012008.
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Abstract Taking IoT devices as the edge nodes is one of the powerful way to offloading the federated task since IoT devices are closer to the data generation end. The aggregation efficiency of federated learning in the IoT environment is inefficiency since the server of federated learning can not know the data quality of heterogeneous IoT device. How to encourage IoT edge clients to participate in federated learning and maximize the aggregation effect of the global model is an important problem. This paper proposes a federated learning aggregation model based on contract theory incentive mechanism. Our experimental results show that the proposed algorithm effectively improves the aggregation efficiency of federated learning compared with FedAvg algorithm.
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Yang, Xun, Shuwen Xiang, Changgen Peng, Weijie Tan, Yue Wang, Hai Liu, and Hongfa Ding. "Federated Learning Incentive Mechanism with Supervised Fuzzy Shapley Value." Axioms 13, no. 4 (April 11, 2024): 254. http://dx.doi.org/10.3390/axioms13040254.
Full textAbstract:
The distributed training of federated machine learning, referred to as federated learning (FL), is discussed in models by multiple participants using local data without compromising data privacy and violating laws. In this paper, we consider the training of federated machine models with uncertain participation attitudes and uncertain benefits of each federated participant, and to encourage all participants to train the desired FL models, we design a fuzzy Shapley value incentive mechanism with supervision. In this incentive mechanism, if the supervision of the supervised mechanism detects that the payoffs of a federated participant reach a value that satisfies the Pareto optimality condition, the federated participant receives a distribution of federated payoffs. The results of numerical experiments demonstrate that the mechanism successfully achieves a fair and Pareto optimal distribution of payoffs. The contradiction between fairness and Pareto-efficient optimization is solved by introducing a supervised mechanism.
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Luo, Yihang, Bei Gong, Haotian Zhu, and Chong Guo. "A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security." Applied Sciences 13, no. 19 (September 22, 2023): 10586. http://dx.doi.org/10.3390/app131910586.
Full textAbstract:
The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy.