19 citations · 23 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
Chenrui Wu, Zexi Li, Fangxin Wang +1
Federated learning (FL) is a distributed framework for collaboratively training with privacy guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalan…
cs.LG2023★ 4 cited
Delving into the Adversarial Robustness of Federated Learning
Jie Zhang, Bo Li, Chen Chen +4
In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples. However, the adversarial robustness of federated learning remains largel…
cs.LG2023★ 19 cited
Revisiting Weighted Aggregation in Federated Learning with Neural Networks
Zexi Li, Tao Lin, Xinyi Shang +1
In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and p…