Academic literature on the topic 'Privacy-preserving federated learning algorithms'
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Journal articles on the topic "Privacy-preserving federated learning algorithms"
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 textDissertations / Theses on the topic "Privacy-preserving federated learning algorithms"
Carlsson, Robert. "Privacy-Preserved Federated Learning : A survey of applicable machine learning algorithms in a federated environment." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424383.
Full textLangelaar, Johannes, and Mattsson Adam Strömme. "Federated Neural Collaborative Filtering for privacy-preserving recommender systems." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446913.
Full textBook chapters on the topic "Privacy-preserving federated learning algorithms"
Lu, Yi, Lei Zhang, Lulu Wang, and Yuanyuan Gao. "Privacy-Preserving and Reliable Federated Learning." In Algorithms and Architectures for Parallel Processing, 346–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95391-1_22.
Full textQiu, Fengyuan, Hao Yang, Lu Zhou, Chuan Ma, and LiMing Fang. "Privacy Preserving Federated Learning Using CKKS Homomorphic Encryption." In Wireless Algorithms, Systems, and Applications, 427–40. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19208-1_35.
Full textBonura, Susanna, Davide Dalle Carbonare, Roberto Díaz-Morales, Marcos Fernández-Díaz, Lucrezia Morabito, Luis Muñoz-González, Chiara Napione, Ángel Navia-Vázquez, and Mark Purcell. "Privacy-Preserving Technologies for Trusted Data Spaces." In Technologies and Applications for Big Data Value, 111–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78307-5_6.
Full textPrabhugaonkar, Gargi Gopalkrishna, Xiaoyan Sun, Xuyu Wang, and Jun Dai. "Deep IoT Monitoring: Filtering IoT Traffic Using Deep Learning." In Silicon Valley Cybersecurity Conference, 120–36. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-24049-2_8.
Full textBonura, Susanna, Davide dalle Carbonare, Roberto Díaz-Morales, Ángel Navia-Vázquez, Mark Purcell, and Stephanie Rossello. "Increasing Trust for Data Spaces with Federated Learning." In Data Spaces, 89–106. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98636-0_5.
Full textZhang, Shuaishuai, Jie Huang, Zeping Zhang, and Chunyang Qi. "Compromise Privacy in Large-Batch Federated Learning via Malicious Model Parameters." In Algorithms and Architectures for Parallel Processing, 63–80. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-22677-9_4.
Full textChowdhury, Alexander, Hasan Kassem, Nicolas Padoy, Renato Umeton, and Alexandros Karargyris. "A Review of Medical Federated Learning: Applications in Oncology and Cancer Research." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 3–24. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08999-2_1.
Full textFan, Tian, Zhixia Zhang, Yang Lan, and Zhihua Cui. "A Many-Objective Anomaly Detection Model for Vehicle Network Based on Federated Learning and Differential Privacy Protection." In Exploration of Novel Intelligent Optimization Algorithms, 52–61. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4109-2_6.
Full textXu, Runhua, Nathalie Baracaldo, Yi Zhou, Annie Abay, and Ali Anwar. "Privacy-Preserving Vertical Federated Learning." In Federated Learning, 417–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96896-0_18.
Full textKim, Kwangjo, and Harry Chandra Tanuwidjaja. "Privacy-Preserving Federated Learning." In Privacy-Preserving Deep Learning, 55–63. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3764-3_5.
Full textConference papers on the topic "Privacy-preserving federated learning algorithms"
Li, Zhenyu. "A Personalized Privacy-Preserving Scheme for Federated Learning." In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2022. http://dx.doi.org/10.1109/eebda53927.2022.9744805.
Full textLi, Qinbin, Bingsheng He, and Dawn Song. "Practical One-Shot Federated Learning for Cross-Silo Setting." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/205.
Full textGuo, Xiaohui. "Federated Learning for Data Security and Privacy Protection." In 2021 12th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE, 2021. http://dx.doi.org/10.1109/paap54281.2021.9720450.
Full textJin, Hongwei, and Xun Chen. "Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/294.
Full textChandran, Pravin, Raghavendra Bhat, Avinash Chakravarthy, and Srikanth Chandar. "Divide-and-Conquer Federated Learning Under Data Heterogeneity." In International Conference on AI, Machine Learning and Applications (AIMLA 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111302.
Full textHu, Rui, Yuanxiong Guo, Hongning Li, Qingqi Pei, and Yanmin Gong. "Privacy-Preserving Personalized Federated Learning." In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). IEEE, 2020. http://dx.doi.org/10.1109/icc40277.2020.9149207.
Full textZhu, Xudong, and Hui Li. "Privacy-preserving Decentralized Federated Deep Learning." In ACM TURC 2021: ACM Turing Award Celebration Conference - China ( ACM TURC 2021). New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472634.3472642.
Full textChattopadhyay, Nandish, Arpit Singh, and Anupam Chattopadhyay. "ROFL: RObust privacy preserving Federated Learning." In 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2022. http://dx.doi.org/10.1109/icdcsw56584.2022.00033.
Full textGao, Dashan, Yang Liu, Anbu Huang, Ce Ju, Han Yu, and Qiang Yang. "Privacy-preserving Heterogeneous Federated Transfer Learning." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005992.
Full textGao, Yuanyuan, Lulu Wang, and Lei Zhang. "Privacy-Preserving Verifiable Asynchronous Federated Learning." In ICSED 2021: 2021 3rd International Conference on Software Engineering and Development. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3507473.3507478.
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