COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy-Reference-Cited by-同舟云学术

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COFEL: Communication-Efficient and Optimized Federated Learning with Local Differential Privacy

Published:2021-06 Issue: Volume: Page:
ISSN:
Container-title:ICC 2021 - IEEE International Conference on Communications
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Author:

Lian Zhuotao,Wang Weizheng,Su Chunhua

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9500243/9500244/09500632.pdf?arnumber=9500632

Cited by 39 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. Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics;Journal of Parallel and Distributed Computing;2024-10

2. Mitigating Demographic Bias of Federated Learning Models via Robust-Fair Domain Smoothing: A Domain-Shifting Approach;2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS);2024-07-23

3. Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief Survey;IEEE Internet of Things Journal;2024-07-15

4. Towards Accurate and Stronger Local Differential Privacy for Federated Learning with Staircase Randomized Response;Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy;2024-06-19

5. Fairness and privacy preserving in federated learning: A survey;Information Fusion;2024-05

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