Journal articles on the topic 'Privacy-preserving federated learning algorithms'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Privacy-preserving federated learning algorithms.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
Cellamare, Matteo, Anna J. van Gestel, Hasan Alradhi, Frank Martin, and Arturo Moncada-Torres. "A Federated Generalized Linear Model for Privacy-Preserving Analysis." Algorithms 15, no. 7 (July 13, 2022): 243. http://dx.doi.org/10.3390/a15070243.
Full textPark, Jaehyoung, and Hyuk Lim. "Privacy-Preserving Federated Learning Using Homomorphic Encryption." Applied Sciences 12, no. 2 (January 12, 2022): 734. http://dx.doi.org/10.3390/app12020734.
Full textThorgeirsson, Adam Thor, and Frank Gauterin. "Probabilistic Predictions with Federated Learning." Entropy 23, no. 1 (December 30, 2020): 41. http://dx.doi.org/10.3390/e23010041.
Full textJiang, Xue, Xuebing Zhou, and Jens Grossklags. "Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder." Proceedings on Privacy Enhancing Technologies 2022, no. 1 (November 20, 2021): 481–500. http://dx.doi.org/10.2478/popets-2022-0024.
Full textZhou, 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.
Full textGong, Xuan, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, and Arun Innanje. "Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 11891–99. http://dx.doi.org/10.1609/aaai.v36i11.21446.
Full textLoftus, Tyler J., Matthew M. Ruppert, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Jeremy A. Balch, Philip A. Efron, Gilbert R. Upchurch, et al. "Federated learning for preserving data privacy in collaborative healthcare research." DIGITAL HEALTH 8 (January 2022): 205520762211344. http://dx.doi.org/10.1177/20552076221134455.
Full textFang, Haokun, and Quan Qian. "Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning." Future Internet 13, no. 4 (April 8, 2021): 94. http://dx.doi.org/10.3390/fi13040094.
Full textAli, 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.
Full textWang, Shengsheng, Shuzhen Lu, and Bin Cao. "Medical Image Object Detection Algorithm for Privacy-Preserving Federated Learning." Journal of Computer-Aided Design & Computer Graphics 33, no. 10 (October 1, 2021): 1153–562. http://dx.doi.org/10.3724/sp.j.1089.2021.18416.
Full textZhao, Jianzhe, Mengbo Yang, Ronglin Zhang, Wuganjing Song, Jiali Zheng, Jingran Feng, and Stan Matwin. "Privacy-Enhanced Federated Learning: A Restrictively Self-Sampled and Data-Perturbed Local Differential Privacy Method." Electronics 11, no. 23 (December 2, 2022): 4007. http://dx.doi.org/10.3390/electronics11234007.
Full textWang, Jie, Li Tian, Guowei Zhu, Chang Liu, and Feng Long. "Indoor Positioning Privacy Protection Method Based on Federated Learning in MEC Environment." Mobile Information Systems 2022 (October 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2311264.
Full textKjamilji, Artrim. "Techniques and Challenges while Applying Machine Learning Algorithms in Privacy Preserving Fashion." Proceeding International Conference on Science and Engineering 3 (April 30, 2020): xix. http://dx.doi.org/10.14421/icse.v3.600.
Full textTursunboev, Jamshid, Yong-Sung Kang, Sung-Bum Huh, Dong-Woo Lim, Jae-Mo Kang, and Heechul Jung. "Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks." Applied Sciences 12, no. 2 (January 11, 2022): 670. http://dx.doi.org/10.3390/app12020670.
Full textBerghout, Tarek, Toufik Bentrcia, Mohamed Amine Ferrag, and Mohamed Benbouzid. "A Heterogeneous Federated Transfer Learning Approach with Extreme Aggregation and Speed." Mathematics 10, no. 19 (September 28, 2022): 3528. http://dx.doi.org/10.3390/math10193528.
Full textHongbin, Fan, and Zhou Zhi. "Privacy-Preserving Data Aggregation Scheme Based on Federated Learning for IIoT." Mathematics 11, no. 1 (January 1, 2023): 214. http://dx.doi.org/10.3390/math11010214.
Full textRaisaro, J. L., Francesco Marino, Juan Troncoso-Pastoriza, Raphaelle Beau-Lejdstrom, Riccardo Bellazzi, Robert Murphy, Elmer V. Bernstam, et al. "SCOR: A secure international informatics infrastructure to investigate COVID-19." Journal of the American Medical Informatics Association 27, no. 11 (July 10, 2020): 1721–26. http://dx.doi.org/10.1093/jamia/ocaa172.
Full textKumar, Dheeraj. "FL-NoiseMap: A Federated Learning-based privacy-preserving Urban Noise-Pollution Measurement System." Noise Mapping 9, no. 1 (January 1, 2022): 128–45. http://dx.doi.org/10.1515/noise-2022-0153.
Full textPeng, Yongqiang, Zongyao Chen, Zexuan Chen, Wei Ou, Wenbao Han, and Jianqiang Ma. "BFLP: An Adaptive Federated Learning Framework for Internet of Vehicles." Mobile Information Systems 2021 (March 2, 2021): 1–18. http://dx.doi.org/10.1155/2021/6633332.
Full textSubramanian, Malliga, Vani Rajasekar, Sathishkumar V. E., Kogilavani Shanmugavadivel, and P. S. Nandhini. "Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification." Electronics 11, no. 24 (December 10, 2022): 4117. http://dx.doi.org/10.3390/electronics11244117.
Full textLi, Xiaochen, Yuke Hu, Weiran Liu, Hanwen Feng, Li Peng, Yuan Hong, Kui Ren, and Zhan Qin. "OpBoost." Proceedings of the VLDB Endowment 16, no. 2 (October 2022): 202–15. http://dx.doi.org/10.14778/3565816.3565823.
Full textWang, Weiya, Geng Yang, Lin Bao, Ke Ma, and Hao Zhou. "A Privacy-Preserving Crowd Flow Prediction Framework Based on Federated Learning during Epidemics." Security and Communication Networks 2022 (October 26, 2022): 1–20. http://dx.doi.org/10.1155/2022/8712597.
Full textZerka, Fadila, Samir Barakat, Sean Walsh, Marta Bogowicz, Ralph T. H. Leijenaar, Arthur Jochems, Benjamin Miraglio, David Townend, and Philippe Lambin. "Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care." JCO Clinical Cancer Informatics, no. 4 (September 2020): 184–200. http://dx.doi.org/10.1200/cci.19.00047.
Full textAsad, Muhammad, Ahmed Moustafa, and Takayuki Ito. "FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning." Applied Sciences 10, no. 8 (April 21, 2020): 2864. http://dx.doi.org/10.3390/app10082864.
Full textSpäth, Julian, Julian Matschinske, Frederick K. Kamanu, Sabina A. Murphy, Olga Zolotareva, Mohammad Bakhtiari, Elliott M. Antman, et al. "Privacy-aware multi-institutional time-to-event studies." PLOS Digital Health 1, no. 9 (September 6, 2022): e0000101. http://dx.doi.org/10.1371/journal.pdig.0000101.
Full textChen, Zunming, Hongyan Cui, Ensen Wu, and Xi Yu. "Dynamic Asynchronous Anti Poisoning Federated Deep Learning with Blockchain-Based Reputation-Aware Solutions." Sensors 22, no. 2 (January 17, 2022): 684. http://dx.doi.org/10.3390/s22020684.
Full textKandati, Dasaradharami Reddy, and Thippa Reddy Gadekallu. "Genetic Clustered Federated Learning for COVID-19 Detection." Electronics 11, no. 17 (August 29, 2022): 2714. http://dx.doi.org/10.3390/electronics11172714.
Full textMiyajima, Hirofumi, Noritaka Shigei, Hiromi Miyajima, and Norio Shiratori. "Machine Learning with Distributed Processing using Secure Divided Data: Towards Privacy-Preserving Advanced AI Processing in a Super-Smart Society." Journal of Networking and Network Applications 2, no. 1 (2022): 48–60. http://dx.doi.org/10.33969/j-nana.2022.020105.
Full textNawrin Tabassum, Mustofa Ahmed, Nushrat Jahan Shorna, MD Mejbah Ur Rahman Sowad, and H M Zabir Haque. "Depression Detection Through Smartphone Sensing: A Federated Learning Approach." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 01 (January 10, 2023): 40–56. http://dx.doi.org/10.3991/ijim.v17i01.35131.
Full textWei, Kang, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farhad Farokhi, Shi Jin, Tony Q. S. Quek, and H. Vincent Poor. "Federated Learning With Differential Privacy: Algorithms and Performance Analysis." IEEE Transactions on Information Forensics and Security 15 (2020): 3454–69. http://dx.doi.org/10.1109/tifs.2020.2988575.
Full textHo, Trang-Thi, Khoa-Dang Tran, and Yennun Huang. "FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information." Sensors 22, no. 10 (May 13, 2022): 3728. http://dx.doi.org/10.3390/s22103728.
Full textElkordy, Ahmed Roushdy, Jiang Zhang, Yahya H. Ezzeldin, Konstantinos Psounis, and Salman Avestimehr. "How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?" Proceedings on Privacy Enhancing Technologies 2023, no. 1 (January 2023): 510–26. http://dx.doi.org/10.56553/popets-2023-0030.
Full textChen, Xuebin, Changyin Luo, Wei Wei, Jingcheng Xu, and Shufen Zhang. "Differential Optimization Federated Incremental Learning Algorithm Based on Blockchain." Electronics 11, no. 22 (November 20, 2022): 3814. http://dx.doi.org/10.3390/electronics11223814.
Full textAgrawal, Shaashwat, Aditi Chowdhuri, Sagnik Sarkar, Ramani Selvanambi, and Thippa Reddy Gadekallu. "Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems." Computational Intelligence and Neuroscience 2021 (December 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/5844728.
Full textYaqoob, Muhammad Mateen, Muhammad Nazir, Abdullah Yousafzai, Muhammad Amir Khan, Asad Ali Shaikh, Abeer D. Algarni, and Hela Elmannai. "Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare." Applied Sciences 12, no. 23 (November 25, 2022): 12080. http://dx.doi.org/10.3390/app122312080.
Full textZhang, Qingsong, Bin Gu, Cheng Deng, and Heng Huang. "Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10896–904. http://dx.doi.org/10.1609/aaai.v35i12.17301.
Full textHou, Ruiqi, Fei Tang, Shikai Liang, and Guowei Ling. "Multi-Party Verifiable Privacy-Preserving Federated k-Means Clustering in Outsourced Environment." Security and Communication Networks 2021 (December 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/3630312.
Full textXu, Bin, Sheng Yan, Shuai Li, and Yidi Du. "A Federated Transfer Learning Framework Based on Heterogeneous Domain Adaptation for Students’ Grades Classification." Applied Sciences 12, no. 21 (October 22, 2022): 10711. http://dx.doi.org/10.3390/app122110711.
Full textLi, Zhong, Xianke Wu, and Changjun Jiang. "Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems." Security and Safety 1 (2022): 2022003. http://dx.doi.org/10.1051/sands/2022003.
Full textWang, Shuyi, and Longxiang Yang. "Securing Dynamic Service Function Chain Orchestration in EC-IoT Using Federated Learning." Sensors 22, no. 23 (November 22, 2022): 9041. http://dx.doi.org/10.3390/s22239041.
Full textLee, 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.
Full textBenedict, Shajulin, Deepumon Saji, Rajesh P. Sukumaran, and Bhagyalakshmi M. "Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications." September 2021 3, no. 3 (August 30, 2021): 196–217. http://dx.doi.org/10.36548/jaicn.2021.3.004.
Full textBemani, Ali, and Niclas Björsell. "Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance." Sensors 22, no. 16 (August 19, 2022): 6252. http://dx.doi.org/10.3390/s22166252.
Full textPark, Sunghwan, Yeryoung Suh, and Jaewoo Lee. "FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs." Sensors 21, no. 2 (January 16, 2021): 600. http://dx.doi.org/10.3390/s21020600.
Full textXuan, 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.
Full textXu, Gang, De-Lun Kong, Xiu-Bo Chen, and Xin Liu. "Lazy Aggregation for Heterogeneous Federated Learning." Applied Sciences 12, no. 17 (August 25, 2022): 8515. http://dx.doi.org/10.3390/app12178515.
Full textOu, Wei, Jianhuan Zeng, Zijun Guo, Wanqin Yan, Dingwan Liu, and Stelios Fuentes. "A homomorphic-encryption-based vertical federated learning scheme for rick management." Computer Science and Information Systems 17, no. 3 (2020): 819–34. http://dx.doi.org/10.2298/csis190923022o.
Full textHUANG, Fang, Zhijun FANG, Zhicai SHI, Lehui ZHUANG, Xingchen LI, and Bo HUANG. "A Federated Domain Adaptation Algorithm Based on Knowledge Distillation and Contrastive Learning." Wuhan University Journal of Natural Sciences 27, no. 6 (December 2022): 499–507. http://dx.doi.org/10.1051/wujns/2022276499.
Full textTao, Jiang, Zhen Gao, and Zhaohui Guo. "Training Vision Transformers in Federated Learning with Limited Edge-Device Resources." Electronics 11, no. 17 (August 23, 2022): 2638. http://dx.doi.org/10.3390/electronics11172638.
Full textShamim, Rejuwan, Md Arshad, and Dr Vinay Pandey. "A Machine Learning Model to Protect Privacy Using Federal Learning with Homomorphy Encryption." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (October 31, 2022): 989–94. http://dx.doi.org/10.22214/ijraset.2022.47120.
Full text