3 papers
cs.LG2024
FedGIG: Graph Inversion from Gradient in Federated Learning
Tianzhe Xiao, Yichen Li, Yining Qi +2
Recent studies have shown that Federated learning (FL) is vulnerable to Gradient Inversion Attacks (GIA), which can recover private training data from shared gradients. However, ex…
cs.LG2024
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Shiwei Li, Wenchao Xu, Haozhao Wang +7
Federated learning is a promising distributed machine learning paradigm that can effectively exploit large-scale data without exposing users' privacy. However, it may incur signifi…
cs.LG2024
Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching
Yichen Li, Wenchao Xu, Haozhao Wang +3
This paper focuses on Federated Domain-Incremental Learning (FDIL) where each client continues to learn incremental tasks where their domain shifts from each other. We propose a no…