17 citations · 45 across the 6 of their papers we have counts for
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cs.LG2024★ 3 cited
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…
cs.LG2024★ 1 cited
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…