no code implementations • 16 Dec 2024 • Hangyu Zhu, Yuxiang Fan, Zhenping Xie
To mitigate this issue, decentralized federated learning (DFL) has been proposed, where all participating clients engage in peer-to-peer communication without a central server.
no code implementations • 28 Sep 2024 • Hangyu Zhu, Liyuan Huang, Zhenping Xie
In this paper, we aim to help researchers better understand and evaluate the effectiveness of privacy attacks on FL.
no code implementations • 30 Aug 2023 • Hangyu Zhu, Yuxiang Fan, Zhenping Xie
While most existing clustered federated learning methods employ either model gradients or inference outputs as metrics for client partitioning, with the goal of grouping similar devices together, may still have heterogeneity within each cluster.
no code implementations • 29 Sep 2021 • Rui Wang, Oğuzhan Ersoy, Hangyu Zhu, Yaochu Jin, Kaitai Liang
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without revealing private local information.
no code implementations • 25 Aug 2021 • Hangyu Zhu, Rui Wang, Yaochu Jin, Kaitai Liang
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data.
no code implementations • 12 Jun 2021 • Hangyu Zhu, Jinjin Xu, Shiqing Liu, Yaochu Jin
Federated learning is an emerging distributed machine learning framework for privacy preservation.
no code implementations • 12 Sep 2020 • Hangyu Zhu, Haoyu Zhang, Yaochu Jin
Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern.
Distributed, Parallel, and Cluster Computing
no code implementations • 4 Mar 2020 • Hangyu Zhu, Yaochu Jin
Federated learning is a distributed machine learning approach to privacy preservation and two major technical challenges prevent a wider application of federated learning.
no code implementations • 18 Dec 2018 • Hangyu Zhu, Yaochu Jin
A scalable method for encoding network connectivity is adapted to federated learning to enhance the efficiency in evolving deep neural networks.