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Journal articles on the topic 'Federated systems'

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Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Conrad, S., W. Hasselbring, U. Hohenstein, R. D. Kutsche, M. Roantree, G. Saake, and F. Saltor. "Engineering federated information systems." ACM SIGMOD Record 28, no. 3 (September 1999): 9–11. http://dx.doi.org/10.1145/333607.333608.

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2

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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3

Jajodia, Sushil, and Duminda Wijesekera. "Security in Federated Database Systems." Information Security Technical Report 6, no. 2 (June 2001): 69–79. http://dx.doi.org/10.1016/s1363-4127(01)00208-4.

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4

Lyu, Weiwei, Xianghong Cheng, and Jinling Wang. "Adaptive Federated IMM Filter for AUV Integrated Navigation Systems." Sensors 20, no. 23 (November 28, 2020): 6806. http://dx.doi.org/10.3390/s20236806.

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High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.

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5

Shokouhi, Milad, and Luo Si. "Federated Search." Foundations and Trends® in Information Retrieval 5, no. 1 (2011): 1–102. http://dx.doi.org/10.1561/1500000010.

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6

L’Amrani, Hasnae, Younès EL Bouzekri EL Idrissi, and Rachida Ajhoun. "Identity Management Systems: Techno-Semantic Interoperability for Heterogeneous Federated Systems." Computer and Information Science 11, no. 3 (July 29, 2018): 102. http://dx.doi.org/10.5539/cis.v11n3p102.

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The identity management domain is a huge research domain. The federated systems proved on theirs legibility to solve a several digital identity issues. However, the problem of interoperability between federations is the researcher first issue. The researchers final goal is creating a federation of federations which is a large meta-system composed of several different federation systems. The previous researchers’ technical interoperability approach solved a part of the above-mentioned issue. However, there are some-others problems in the communication process between federated systems. In this work, the researcher target the semantic interoperability as a solution to solve the exchange of attribute issue among heterogeneous federated systems, because there is a significant need of managing the users’ attributes coming from different federations. Therefore, the researcher proposed a semantic layer to enhance the previous technical approach with the aim to guarantee the exchange of attribute that has the same semantic signification but a different representation, all that based on a mapping and matching between different anthologies. This approach will be applied to the academic domain as the researcher application domain.

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7

L'Amrani, Hasnae, Younès El Bouzekri El Idrissi, and Rachida Ajhoun. "Technical Interoperability to Solve Cross-Domain Issues Among Federation Systems." International Journal of Smart Security Technologies 7, no. 1 (January 2020): 21–40. http://dx.doi.org/10.4018/ijsst.2020010102.

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Digital identity management with the metamorphosis of web services enforces new security challenges. A set of identity management systems exists to deal with these identities, alongside the goal of improving user experience and gain secure access. Nowadays, one faces a large number of heterogeneous identity management approaches. This study treated several identity management systems. The federated system makes proof of it eligibility for the identity management. Thus, the researcher interest is on the federated model. Since it consists of the distribution of digital identity between different security domains. The base of security domains is a trust agreement between the entities in communication. Federated identity management faces the problem of interoperability between heterogeneous federated systems. This study is an approach of a technical interoperability between the federations. The authors propose an approach that will permit inter-operation and exchange identity information among heterogeneous federations.

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8

Lübke, Daniel, and Jorge Marx Gómez. "Developing and Customizing Federated ERP Systems." International Journal of Enterprise Information Systems 5, no. 3 (July 2009): 47–59. http://dx.doi.org/10.4018/jeis.2009070104.

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9

Conrad, S., B. Eaglestone, W. Hasselbring, M. Roantree, M. Schöhoff, M. Strässler, M. Vermeer, and F. Saltor. "Research issues in federated database systems." ACM SIGMOD Record 26, no. 4 (December 1997): 54–56. http://dx.doi.org/10.1145/271074.271089.

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10

Thuraisingham, Bhavani. "Security issues for federated database systems." Computers & Security 13, no. 6 (1994): 509–25. http://dx.doi.org/10.1016/0167-4048(91)90139-5.

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11

BLANCO, JOSÉ MIGUEL, ARANTZA ILLARRAMENDI, and ALFREDO GOÑI. "BUILDING A FEDERATED RELATIONAL DATABASE SYSTEM: AN APPROACH USING A KNOWLEDGE-BASED SYSTEM." International Journal of Cooperative Information Systems 03, no. 04 (December 1994): 415–55. http://dx.doi.org/10.1142/s0218215794000211.

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Due to the emerging interest in integrating different application environments, there have been many recent proposals for federated systems. In this paper, a federated system that permits the integration of heterogeneous relational databases using a terminological knowledge representation system is presented. In particular, two of the system's components: the translator and the integrator are explained in depth. The translator permits one to obtain a terminology from a relational schema, either semiautomatically, by expressing database properties, or manually, by using a set of predefined operations. In turn, the integrator generates a federated terminology by integrating several terminologies using the semantics expressed as correspondences between the data elements of different terminologies. Unlike many other approaches, the use of a terminological system permits us to obtain a semantically richer federated terminology and, at the same time, define a wider and more consistent integration process.

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12

Ali, Waqar, Rajesh Kumar, Zhiyi Deng, Yansong Wang, and Jie Shao. "A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems." Computer Journal 64, no. 7 (April 30, 2021): 1016–27. http://dx.doi.org/10.1093/comjnl/bxab025.

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Abstract Privacy protection is one of the key concerns of users in recommender system-based consumer markets. Popular recommendation frameworks such as collaborative filtering (CF) suffer from several privacy issues. Federated learning has emerged as an optimistic approach for collaborative and privacy-preserved learning. Users in a federated learning environment train a local model on a self-maintained item log and collaboratively train a global model by exchanging model parameters instead of personalized preferences. In this research, we proposed a federated learning-based privacy-preserving CF model for context-aware recommender systems that work with a user-defined collaboration protocol to ensure users’ privacy. Instead of crawling users’ personal information into a central server, the whole data are divided into two disjoint parts, i.e. user data and sharable item information. The inbuilt power of federated architecture ensures the users’ privacy concerns while providing considerably accurate recommendations. We evaluated the performance of the proposed algorithm with two publicly available datasets through both the prediction and ranking perspectives. Despite the federated cost and lack of open collaboration, the overall performance achieved through the proposed technique is comparable with popular recommendation models and satisfactory while providing significant privacy guarantees.

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13

Akhtyamov, Rustam, Rob Vingerhoeds, and Alessandro Golkar. "Identifying Retrofitting Opportunities for Federated Satellite Systems." Journal of Spacecraft and Rockets 56, no. 3 (May 2019): 620–29. http://dx.doi.org/10.2514/1.a34196.

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14

Hasselbring, W., W. J. van den Heuvel, G. J. Houben, R. D. Kutsche, B. Rieger, M. Roantree, and K. Subieta. "Research and practice in federated information systems." ACM SIGMOD Record 29, no. 4 (December 2000): 16–18. http://dx.doi.org/10.1145/369275.369277.

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15

Grogan, Paul T., Koki Ho, Alessandro Golkar, and Olivier L. de Weck. "Multi-Actor Value Modeling for Federated Systems." IEEE Systems Journal 12, no. 2 (June 2018): 1193–202. http://dx.doi.org/10.1109/jsyst.2016.2626981.

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16

Xianjia, Yu, Jorge Peña Queralta, Jukka Heikkonen, and Tomi Westerlund. "Federated Learning in Robotic and Autonomous Systems." Procedia Computer Science 191 (2021): 135–42. http://dx.doi.org/10.1016/j.procs.2021.07.041.

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17

Li, Zi Yu, Yan Liu, Ping Zhu, and Cheng Ying. "Federated Particle Filter Technology Based on JIDS/SINS/GPS Integrated Navigation System." Applied Mechanics and Materials 347-350 (August 2013): 1544–48. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1544.

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In multi-sensor integrated navigation systems, when sub-systems are non-linear and with Gaussian noise, the federated Kalman filter commonly used generates large error or even failure when estimating the global fusion state. This paper, taking JIDS/SINS/GPS integrated navigation system as example, proposes a federated particle filter technology to solve problems above. This technology, combining the particle filter with the federated Kalman filter, can be applied to non-linear non-Gaussian integrated system. It is proved effective in information fusion algorithm by simulated application, where the navigation information gets well fused.

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18

Xuan, Shichang, Ming Jin, Xin Li, Zhaoyuan Yao, Wu Yang, and Dapeng Man. "DAM-SE: A Blockchain-Based Optimized Solution for the Counterattacks in the Internet of Federated Learning Systems." Security and Communication Networks 2021 (July 1, 2021): 1–14. http://dx.doi.org/10.1155/2021/9965157.

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The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the “free-rider attacks” and “model poisoning attacks” in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.

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19

Hwang, S. Y., E. P. Lim, H. R. Yang, S. Musukula, K. Mediratta, M. Ganesh, D. Clements, J. Stenoien, and J. Srivastava. "The MYRIAD federated database prototype." ACM SIGMOD Record 23, no. 2 (June 1994): 518. http://dx.doi.org/10.1145/191843.191986.

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20

Xu, Shuqing, Haiyin Zhou, Jiongqi Wang, Zhangming He, and Dayi Wang. "SINS/CNS/GNSS Integrated Navigation Based on an Improved Federated Sage–Husa Adaptive Filter." Sensors 19, no. 17 (September 3, 2019): 3812. http://dx.doi.org/10.3390/s19173812.

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Among the methods of the multi-source navigation filter, as a distributed method, the federated filter has a small calculation amount with Gaussian state noise, and it is easy to achieve global optimization. However, when the state noise is time-varying or its initial estimation is not accurate, there will be a big difference with the true value in the result of the federated filter. For the systems with time-varying noise, adaptive filter is widely used for its remarkable advantages. Therefore, this paper proposes a federated Sage–Husa adaptive filter for multi-source navigation systems with time-varying or mis-estimated state noise. Because both the federated and the adaptive principles are different in updating the covariance of the state noise, it is required to weight the two updating methods to obtain a combined method with stability and adaptability. In addition, according to the characteristics of the system, the weighting coefficient is formed by the exponential function. This federated adaptive filter is applied to the SINS/CNS/GNSS integrated navigation, and the simulation results show that this method is effective.

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21

Lo, Sin Kit, Qinghua Lu, Chen Wang, Hye-Young Paik, and Liming Zhu. "A Systematic Literature Review on Federated Machine Learning." ACM Computing Surveys 54, no. 5 (June 2021): 1–39. http://dx.doi.org/10.1145/3450288.

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Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results and identify future trends to encourage researchers to advance their current work.

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22

Korah, Abe, and Erin Dorris Cassidy. "Students and Federated Searching." Reference & User Services Quarterly 49, no. 4 (June 1, 2010): 325–32. http://dx.doi.org/10.5860/rusq.49n4.325.

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23

Karasawa, Mizuki, John Hover, and Shigeki Misawa. "Federated User Account Management." EPJ Web of Conferences 245 (2020): 07058. http://dx.doi.org/10.1051/epjconf/202024507058.

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BNL SDCC (Scientific Data and Computing Center) recently deployed a centralized identity management solution to support Single Sign On (SSO) authentication across multiple IT systems. The system supports federated login access via CILogon and InCommon and multi-factor authentication (MFA) to meet security standards for various application and services such as Jupyterhub / Invenio that are provided to the SDCC user community. CoManage (cloud-based) and FreeIPA / Keycloak (local) are utilized to provided complex authorization for authenticated users. This talk will focus on technical overviews and strategies to tackle the challenges/obstacles in our facility.

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24

Zhang, Chen, Yu Xie, Hang Bai, Bin Yu, Weihong Li, and Yuan Gao. "A survey on federated learning." Knowledge-Based Systems 216 (March 2021): 106775. http://dx.doi.org/10.1016/j.knosys.2021.106775.

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25

Sanchez Net, Marc, Iñigo del Portillo, Bruce Cameron, and Edward F. Crawley. "Architecting Information Security Services for Federated Satellite Systems." Journal of Aerospace Information Systems 14, no. 8 (January 2017): 439–50. http://dx.doi.org/10.2514/1.i010425.

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26

De Capitani di Vimercati, Sabrina, and Pierangela Samarati. "Authorization specification and enforcement in federated database systems*." Journal of Computer Security 5, no. 2 (April 1, 1997): 155–88. http://dx.doi.org/10.3233/jcs-1997-5205.

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27

Chen, Gang, Abdolhossein Sarrafzadeh, and Shaoning Pang. "Service Provision Control in Federated Service Providing Systems." IEEE Transactions on Parallel and Distributed Systems 24, no. 3 (March 2013): 587–600. http://dx.doi.org/10.1109/tpds.2012.150.

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28

Canini, Marco, Vojin Jovanović, Daniele Venzano, Dejan Novaković, and Dejan Kostić. "Online testing of federated and heterogeneous distributed systems." ACM SIGCOMM Computer Communication Review 41, no. 4 (October 22, 2011): 434–35. http://dx.doi.org/10.1145/2043164.2018507.

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29

Birrell, Eleanor, and Fred B. Schneider. "Federated Identity Management Systems: A Privacy-Based Characterization." IEEE Security & Privacy 11, no. 5 (September 2013): 36–48. http://dx.doi.org/10.1109/msp.2013.114.

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30

Ciampi, Mario, Giuseppe De Pietro, Christian Esposito, Mario Sicuranza, and Paolo Donzelli. "A federated interoperability architecture for health information systems." International Journal of Internet Protocol Technology 7, no. 4 (2013): 189. http://dx.doi.org/10.1504/ijipt.2013.058646.

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31

Du, Yongjie, Deyun Zhou, Yu Xie, Jiao Shi, and Maoguo Gong. "Federated matrix factorization for privacy-preserving recommender systems." Applied Soft Computing 111 (November 2021): 107700. http://dx.doi.org/10.1016/j.asoc.2021.107700.

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32

Popowich, Sam. "Proxying the Data Body: Artificial Intelligence, Federated Identity, and Machinic Subjection." Journal of Contemporary Issues in Education 15, no. 1 (June 28, 2020): 35–50. http://dx.doi.org/10.20355/jcie29410.

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Academic libraries have recently seen a shift from self-management of user-authentication of licensed resources themselves, to cloud-based implementations of "federated identity" technologies. Such technologies aim to solve the problems of fragile access to licensed resources while also better protecting publishers' intellectual property. However, federated identity systems raise a host of issues regarding privacy, surveillance, machinic subjection, and algorithmic governance. This paper traces the development of federated identity systems out of earlier authentication processes, shows how such systems use artificial intelligence techniques to create a trackable "data body" for each student, and then analyzes this whole procedure through the critical theories of Maurizio Lazzarato and Bernard Stiegler. In conclusion, the article argues that the emergent nature of the "data body" creates ambiguity between the hyper-control of contemporary technologies and the possibility of resisting them.

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33

De Vita, Fabrizio, and Dario Bruneo. "Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems." Journal of Sensor and Actuator Networks 9, no. 4 (December 18, 2020): 59. http://dx.doi.org/10.3390/jsan9040059.

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During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy.

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34

Wang, Lijun, Sisi Wang, and Wenzhi Yang. "Adaptive federated filter for multi-sensor nonlinear system with cross-correlated noises." PLOS ONE 16, no. 2 (February 19, 2021): e0246680. http://dx.doi.org/10.1371/journal.pone.0246680.

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This paper presents an adaptive approach to the federated filter for multi-sensor nonlinear systems with cross-correlations between process noise and local measurement noise. The adaptive Gaussian filter is used as the local filter of the federated filter for the first time, which overcomes the performance degradation caused by the cross-correlated noises. Two kinds of adaptive federated filters are proposed, one uses a de-correlation framework as local filter, and the subfilter of the other one is defined as a Gaussian filter with correlated noises at the same-epoch, and much effort is made to verify the theoretical equivalence of the two algorithms in the nonlinear fusion system. Simulation results show that the proposed algorithms are superior to the traditional federated filter and Gaussian filter with same-paced correlated noises, and the equivalence between the proposed algorithms and high degree cubature federated filter is also demonstrated.

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35

Chang, Chai Fung, Pong Heng Tan, and Soh Khum Tam. "2.1.3 Managing Systems of Systems Interoperability - Federated SOA and Reference Architectures." INCOSE International Symposium 19, no. 1 (July 2009): 199–212. http://dx.doi.org/10.1002/j.2334-5837.2009.tb00945.x.

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36

Arguello, Jaime. "Federated search in heterogeneous environments." ACM SIGIR Forum 46, no. 1 (May 20, 2012): 78–79. http://dx.doi.org/10.1145/2215676.2215686.

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37

Jiang, Haifeng, Dengfeng Gao, and Wen-Syan Li. "Improving parallelism of federated query processing." Data & Knowledge Engineering 64, no. 3 (March 2008): 511–33. http://dx.doi.org/10.1016/j.datak.2007.05.007.

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38

Hwang, San-Yih, Jaideep Srivastava, and Jianzhong Li. "Transaction recovery in federated autonomous databases." Distributed and Parallel Databases 2, no. 2 (April 1994): 151–82. http://dx.doi.org/10.1007/bf01267325.

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39

Jang, Suyeon, Hyun Woo Oh, Young Hyun Yoon, Dong Hyun Hwang, Won Sik Jeong, and Seung Eun Lee. "A Multi-Core Controller for an Embedded AI System Supporting Parallel Recognition." Micromachines 12, no. 8 (July 21, 2021): 852. http://dx.doi.org/10.3390/mi12080852.

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Recent advances in artificial intelligence (AI) technology encourage the adoption of AI systems for various applications. In most deployments, AI-based computing systems adopt the architecture in which the central server processes most of the data. This characteristic makes the system use a high amount of network bandwidth and can cause security issues. In order to overcome these issues, a new AI model called federated learning was presented. Federated learning adopts an architecture in which the clients take care of data training and transmit only the trained result to the central server. As the data training from the client abstracts and reduces the original data, the system operates with reduced network resources and reinforced data security. A system with federated learning supports a variety of client systems. To build an AI system with resource-limited client systems, composing the client system with multiple embedded AI processors is valid. For realizing the system with this architecture, introducing a controller to arbitrate and utilize the AI processors becomes a stringent requirement. In this paper, we propose an embedded AI system for federated learning that can be composed flexibly with the AI core depending on the application. In order to realize the proposed system, we designed a controller for multiple AI cores and implemented it on a field-programmable gate array (FPGA). The operation of the designed controller was verified through image and speech applications, and the performance was verified through a simulator.

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40

Cheng, Wenzhi, Wei Ou, Xiangdong Yin, Wanqin Yan, Dingwan Liu, and Chunyan Liu. "A Privacy-Protection Model for Patients." Security and Communication Networks 2020 (December 10, 2020): 1–12. http://dx.doi.org/10.1155/2020/6647562.

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The collection and analysis of patient cases can effectively help researchers to extract case feature and to achieve the objectives of precision medicine, but it may cause privacy issues for patients. Although encryption is a good way to protect privacy, it is not conducive to the sharing and analysis of medical cases. In order to address this problem, this paper proposes a federated learning verification model, which combines blockchain technology, homomorphic encryption, and federated learning technology to effectively solve privacy issues. Moreover, we present a FL-EM-GMM Algorithm (Federated Learning Expectation Maximization Gaussian Mixture Model Algorithm), which can make model training without data exchange for protecting patient’s privacy. Finally, we conducted experiments on the federated task of datasets from two organizations in our model system, where the data has the same sample ID with different subset features, and this system is capable of handling privacy and security issues. The results show that the model was trained by our system with better usability, security, and higher efficiency, which is compared with the model trained by traditional machine learning methods.

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41

Zhou, Zhou, Youliang Tian, and Changgen Peng. "Privacy-Preserving Federated Learning Framework with General Aggregation and Multiparty Entity Matching." Wireless Communications and Mobile Computing 2021 (June 26, 2021): 1–14. http://dx.doi.org/10.1155/2021/6692061.

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The requirement for data sharing and privacy has brought increasing attention to federated learning. However, the existing aggregation models are too specialized and deal less with users’ withdrawal issue. Moreover, protocols for multiparty entity matching are rarely covered. Thus, there is no systematic framework to perform federated learning tasks. In this paper, we systematically propose a privacy-preserving federated learning framework (PFLF) where we first construct a general secure aggregation model in federated learning scenarios by combining the Shamir secret sharing with homomorphic cryptography to ensure that the aggregated value can be decrypted correctly only when the number of participants is greater than t . Furthermore, we propose a multiparty entity matching protocol by employing secure multiparty computing to solve the entity alignment problems and a logistic regression algorithm to achieve privacy-preserving model training and support the withdrawal of users in vertical federated learning (VFL) scenarios. Finally, the security analyses prove that PFLF preserves the data privacy in the honest-but-curious model, and the experimental evaluations show PFLF attains consistent accuracy with the original model and demonstrates the practical feasibility.

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42

Lluch, Ignasi, and Alessandro Golkar. "Design Implications for Missions Participating in Federated Satellite Systems." Journal of Spacecraft and Rockets 52, no. 5 (September 2015): 1361–74. http://dx.doi.org/10.2514/1.a33172.

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43

Thomas, Manoj A., Victoria Y. Yoon, and Richard Redmond. "Extending Loosely Coupled Federated Information Systems Using Agent Technology." International Journal of Intelligent Information Technologies 3, no. 3 (July 2007): 1–20. http://dx.doi.org/10.4018/jiit.2007070101.

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44

Sicuranza, Mario, Mario Ciampi, Giuseppe De Pietro, and Christian Esposito. "Secure healthcare data sharing among federated health information systems." International Journal of Critical Computer-Based Systems 4, no. 4 (2013): 349. http://dx.doi.org/10.1504/ijccbs.2013.059023.

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45

Wang, Shiqiang, Tiffany Tuor, Theodoros Salonidis, Kin K. Leung, Christian Makaya, Ting He, and Kevin Chan. "Adaptive Federated Learning in Resource Constrained Edge Computing Systems." IEEE Journal on Selected Areas in Communications 37, no. 6 (June 2019): 1205–21. http://dx.doi.org/10.1109/jsac.2019.2904348.

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46

Ren, Jinke, Guanding Yu, and Guangyao Ding. "Accelerating DNN Training in Wireless Federated Edge Learning Systems." IEEE Journal on Selected Areas in Communications 39, no. 1 (January 2021): 219–32. http://dx.doi.org/10.1109/jsac.2020.3036971.

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Joint, Nicholas. "Federated search engines and the development of library systems." Library Review 57, no. 9 (October 10, 2008): 653–59. http://dx.doi.org/10.1108/00242530810911770.

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FENG, Ji, Qi-Zhi CAI, and Yuan JIANG. "Towards training time attacks for federated machine learning systems." SCIENTIA SINICA Informationis 51, no. 6 (May 26, 2021): 900. http://dx.doi.org/10.1360/ssi-2019-0145.

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Preuveneers, Davy, Vera Rimmer, Ilias Tsingenopoulos, Jan Spooren, Wouter Joosen, and Elisabeth Ilie-Zudor. "Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study." Applied Sciences 8, no. 12 (December 18, 2018): 2663. http://dx.doi.org/10.3390/app8122663.

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Abstract:

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

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Choi, Jinho, and Shiva Raj Pokhrel. "Federated Learning With Multichannel ALOHA." IEEE Wireless Communications Letters 9, no. 4 (April 2020): 499–502. http://dx.doi.org/10.1109/lwc.2019.2960243.

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