2 papers
cs.LG2025
Survey of Privacy Threats and Countermeasures in Federated Learning
Masahiro Hayashitani, Junki Mori, Isamu Teranishi
Federated learning is widely considered to be as a privacy-aware learning method because no training data is exchanged directly between clients. Nevertheless, there are threats to…
cs.LG2024
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data
Junki Mori, Kosuke Kihara, Taiki Miyagawa +3
Federated learning (FL) commonly assumes that the server or some clients have labeled data, which is often impractical due to annotation costs and privacy concerns. Addressing this…