most citedRobust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization

4 citations · 4 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2022

DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics

Renjie Pi, Weizhong Zhang, Yueqi Xie +4

The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, whi…

cs.LG2022★ 4 cited

Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization

Yueqi Xie, Weizhong Zhang, Renjie Pi +4

Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…