3 citations · 3 across the 4 of their papers we have counts for
4 papers
CARVY-FL: Client Anticlustering for Robust Voting in Provably Secure Federated Learning
Masaki Nakada, Honoka Anada, Tatsuya Kaneko +3
Federated learning (FL) enables collaborative training without directly sharing raw data, but remains vulnerable to malicious clients. Voting-based FL improves robustness by partit…
TRIP-Shapley: Propagation-Aware Contribution Attribution Framework for Peer-to-Peer Federated Learning
Honoka Anada, Tatsuya Kaneko, Yafei Ou +1
Contribution attribution is a fundamental problem in federated learning (FL), yet it remains challenging in peer-to-peer FL (P2PFL), where participant influence propagates through…
PRIOT: Pruning-Based Integer-Only Transfer Learning for Embedded Systems
Honoka Anada, Sefutsu Ryu, Masayuki Usui +2
On-device transfer learning is crucial for adapting a common backbone model to the unique environment of each edge device. Tiny microcontrollers, such as the Raspberry Pi Pico, are…
Federated Learning with Relative Fairness
Shogo Nakakita, Tatsuya Kaneko, Shinya Takamaeda-Yamazaki +1
This paper proposes a federated learning framework designed to achieve \textit{relative fairness} for clients. Traditional federated learning frameworks typically ensure absolute f…