Hideya Ochiai

Feature Distribution Matching for Federated Domain Generalization

2022 · arXiv (Cornell University)

<p>Multi-source domain adaptation has been intensively studied. The distribution shift in features inherent to specific domains causes the negative transfer problem, degrading a model’s generality to unseen tasks. In Federated Learning (FL), learned model parameters …

Homogeneous Learning: Self-Attention Decentralized Deep Learning

2021

Federated learning (FL) has been facilitating privacy-preserving deep learning in many walks of life such as medical image classification, network intrusion detection, and so forth. Whereas it necessitates a central parameter server for model aggregation, …

Decentralized P2P Federated Learning on Ad-hoc Like Networks with Non-IID Dataset

2022

In the last few decades, Federated Learning (FL) is proposed in order to perform Machine Learning (ML) tasks in a distributed manner while protecting users' privacy and data. However, most of the traditional FL methods …

Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis

2022 · 2022 International Joint Conference on Neural Networks (IJCNN)

Model poisoning attacks on federated learning intrude in the entire system via compromising an edge model, resulting in malfunctioning of machine learning models. Such compromised models are tampered with to perform adversary-desired behaviors. In particular, …

WAFL-GAN: Wireless Ad Hoc Federated Learning for Distributed Generative Adversarial Networks

2023

Diverse images are needed to train Generative Adversarial Network (GAN) with diverse image output, but privacy is a major issue. To protect privacy, federated learning has been proposed, but in conventional federated learning, the parameter …