Effects of Quantization on Federated Learning with Local Differential Privacy
Author:
Affiliation:
1. Technical University of Dresden,Chair of Information Theory and Machine Learning,Dresden,Germany
2. Linkö ping University,Information Coding Division,Linköping,Sweden
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10000063/10000593/10000632.pdf?arnumber=10000632
Reference29 articles.
1. Federated Learning with Local Differential Privacy: Trade-Offs Between Privacy, Utility, and Communication
2. Wireless Federated Learning with Local Differential Privacy
3. Federated Learning: Challenges, Methods, and Future Directions
4. Advances and Open Problems in Federated Learning
5. Local Differential Privacy-Based Federated Learning for Internet of Things
Cited by 2 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
1. The Effect of Quantization in Federated Learning:A Rényi Differential Privacy Perspective;2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom);2024-07-08
2. Binary Federated Learning with Client-Level Differential Privacy;GLOBECOM 2023 - 2023 IEEE Global Communications Conference;2023-12-04
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