Author:
Liu Fengxia,Zheng Zhiming,Shi Yexuan,Tong Yongxin,Zhang Yi
Abstract
AbstractFederated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets. To enhance privacy in federated learning, multi-party computation can be leveraged for secure communication and computation during model training. This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy, as well as the corresponding optimization techniques to improve model accuracy and training efficiency. We also pinpoint future directions to deploy federated learning to a wider range of applications.
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science,Theoretical Computer Science
Reference90 articles.
1. Konečný J, McMahan H B, Yu F X, Richtárik P, Suresh A T, Bacon D Federated learning: strategies for improving communication efficiency. 2016, arXiv preprint arXiv: 1610.05492
2. Yang Q, Liu Y, Chen T, Tong Y. Federated machine learning: concept and applications. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 12
3. Tong Y, Zeng Y, Zhou Z, Liu B, Shi Y, Li S, Xu K, Lv W. Federated computing: query, learning, and beyond. IEEE Data Engineering Bulletin, 2023, 46(1): 9–26
4. Zhang K, Song X, Zhang C, Yu S. Challenges and future directions of secure federated learning: a survey. Frontiers of Computer Science, 2022, 16(5): 165817
5. Chen Y, Qin X, Wang J, Yu C, Gao W. FedHealth: a federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems, 2020, 35(4): 83–93
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献