arXiv subjects

Hiroto Sawada

Publications and source records attributed to Hiroto Sawada.

4 recordsLinked to original sources

arXiv2026

Designing Signals for Deterrence

States and alliances spend enormous resources signaling resolve to deter adversaries. Two canonical strategies are burning money (sunk costs) and burning bridges (audience costs). Large audience costs can deter effectively, but they may be infeasible and, even when feasible, can increase the likelihood of war relative to burning money. We propose a signaling design in which the defender retains the flexibility to fight or back down but commits ex ante, before learning its resolve, to a rule mapping realized resolve into costly actions. When audience costs are limited, this design raises defender payoffs while reducing the likelihood of war. We discuss implementation through laws and automated algorithms. We also characterize defender payoffs when audience costs are chosen endogenously from a feasible set, showing that lowering the maximum feasible audience cost can raise defender payoffs, and we compare these payoffs with those under the standard burning-money setting without commitment.

econ.TH↗

arXiv2026

Enhancing Privacy in Federated Learning via Dual Obfuscation of Gradients and Training Images

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 shared model updates. To address this issue, this paper proposes a dual obfuscation method that enhances robustness against image restoration attacks by jointly obfuscating updated information and training images. The proposed method combines a robustness enhancement technique based on random binary weights, which randomly sets a portion of gradient elements to zero, with an image encryption technique. These techniques provide complementary protection by reducing the amount of original gradient information available to an attacker and the visual interpretability of reconstructed images, respectively. Furthermore, the image encryption technique allows independent keys to be used for each client and each image, avoiding explicit key sharing. Experimental results on an image classification task using a Vision Transformer (ViT) show that the proposed method reduces the visual information recovered by Attention Privacy Leakage (APRIL) under the evaluated settings without causing additional degradation in classification performance beyond that caused by image encryption. Although the proposed combination does not provide an absolute security guarantee, the results demonstrate the potential benefit of combining gradient modification and image encryption for privacy-enhanced federated learning.

cs.CR↗

arXiv2026

FLRSP: Privacy-Preserving Federated Learning Using Randomly Selected Model Parameters

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 (DNN) models require a huge amount of training data in general, but model training raises privacy concerns when dealing with sensitive or personal information. Federated learning is a distributed machine learning framework in which multiple clients and a server train a model collaboratively. However, if the shared updates are compromised, an attacker may reconstruct the original training data. In addition, previous methods for improving robustness generally reduce the accuracy. To overcome these issues, in our method called federated learning using randomly selected model parameters (FLRSP), model parameters computed in each local server are randomly selected and shared to update a global model in a central server. In experiments, image classification tasks were carried out on the ResNet34 architecture and the Vision Transformer (ViT) under the use of Federated Stochastic Gradient Descent (FedSGD) and Federated Averaging (FedAvg), and the results demonstrated our method's effectiveness in terms of image classification accuracy and robustness against state-of-the-art attacks compared with previous methods.

cs.CR↗

arXiv2024

Enhancing Security Using Random Binary Weights in Privacy-Preserving Federated Learning

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 by collecting updated information without collecting raw data from each client. However, the problem is that this raw data may be inferred from the updated information. Conventional data-guessing countermeasures (security enhancement methods) for addressing this issue have a trade-off relationship between privacy protection strength and learning efficiency, and they generally degrade model performance. In this paper, we propose a novel method of federated learning that does not degrade model performance and that is robust against data-guessing attacks on updated information. In the proposed method, each client independently prepares a sequence of binary (0 or 1) random numbers, multiplies it by the updated information, and sends it to a server for model learning. In experiments, the effectiveness of the proposed method is confirmed in terms of model performance and resistance to the APRIL (Attention PRIvacy Leakage) restoration attack.

cs.CR↗