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cs.LG2024
Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets
Xingrun Yan, Shiyuan Zuo, Rongfei Fan +4
In a real federated learning (FL) system, communication overhead for passing model parameters between the clients and the parameter server (PS) is often a bottleneck. Hierarchical…
cs.LG2024
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but is vulnerable to Byzantine attacks and data heterogeneity, which can…
cs.LG2024
Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), wh…