Robust Federated Learning: A Heterogeneity Index Based Clustering Approach

Author:

Pene Papa,Tian Pu,Liao Weixian,Wang Qianlong,Yu Wei

Publisher

Springer Nature Switzerland

Reference43 articles.

1. McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data, pp 1273–1282

2. Li Q, Wen Z, Wu Z, Hu S, Wang N, Li Y, Liu X, He B (2021) A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Trans Knowl Data Eng 1

3. Chen Z, Tian P, Liao W, Yu W (2021) Towards multi-party targeted model poisoning attacks against federated learning systems. High-Confid Comput 1(1):100002

4. Tian P, Liao W, Yu W, Blasch E (2022) WSCC: a weight-similarity-based client clustering approach for non-iid federated learning. IEEE Internet Things J 9(20):20 243–20 256

5. Chen Z, Liao W, Hua K, Lu C, Yu W (2021) Towards asynchronous federated learning for heterogeneous edge-powered internet of things. Digit Commun Netw 7(3):317–326

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