3 papers
cs.CR2026
Enhancing Privacy in Federated Learning via Dual Obfuscation of Gradients and Training Images
Yuki Itabashi, Hiroto Sawada, Mare Hirose +2
Federated learning enables collaborative model training while keeping data locally at each client; however, recent studies have shown that training data can be reconstructed from s…
cs.CR2026
FLRSP: Privacy-Preserving Federated Learning Using Randomly Selected Model Parameters
Hiroto Sawada, Shoko Imaizumi, Hitoshi Kiya
In this paper, we propose a method for privacy-preserving federated learning that uses randomly selected model parameters to update global models. High-quality deep neural networks…
cs.CR2024
Enhancing Security Using Random Binary Weights in Privacy-Preserving Federated Learning
Hiroto Sawada, Shoko Imaizumi, Hitoshi Kiya
In this paper, we propose a novel method for enhancing security in privacy-preserving federated learning using the Vision Transformer. In federated learning, learning is performed…