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…
Dataflow-Oriented Classification and Performance Analysis of GPU-Accelerated Homomorphic Encryption
Ai Nozaki, Takuya Kojima, Hideki Takase +1
Fully Homomorphic Encryption (FHE) enables secure computation over encrypted data, but its computational cost remains a major obstacle to practical deployment. To mitigate this ove…
Hetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT Devices
Yuki Oda, Yuta Ono, Hiroshi Nakamura +1
The continuous scaling of deep neural networks has fundamentally transformed machine learning, with larger models demonstrating improved performance across diverse tasks. This grow…
Exploring the Possibility of TypiClust for Low-Budget Federated Active Learning
Yuta Ono, Hiroshi Nakamura, Hideki Takase
Federated Active Learning (FAL) seeks to reduce the burden of annotation under the realistic constraints of federated learning by leveraging Active Learning (AL). As FAL settings m…