4 papers
Designing Signals for Deterrence
Hiroto Sawada, Raphael Boleslavsky, Kristopher W. Ramsay +1
States and alliances spend enormous resources signaling resolve to deter adversaries. Two canonical strategies are burning money (sunk costs) and burning bridges (audience costs).…
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…
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…
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…