3 citations · 3 across the 2 of their papers we have counts for
3 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…
How to Evaluate Participant Contributions in Decentralized Federated Learning
Honoka Anada, Tatsuya Kaneko, Shinya Takamaeda-Yamazaki
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without sharing local data. In particular, decentralized FL (DFL), where clients e…
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…