3 citations · 3 across the 3 of their papers we have counts for
4 papers
TALLOP: Joint Optimization of Local Training and Communication Compression for Time-Constrained Federated Learning
Honoka Anada, Yafei Ou, Shinya Takamaeda-Yamazaki
Minimizing the wall-clock time required to reach a target accuracy is crucial in federated learning (FL), where many applications require frequent model updates as data distributio…
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…