FedSeq: A Hybrid Federated Learning Framework Based on Sequential In-Cluster Training-Reference-Cited by-同舟云学术

FedSeq: A Hybrid Federated Learning Framework Based on Sequential In-Cluster Training

Published:2023-09 Issue:3 Volume:17 Page:4038-4049
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Chen Zhikun¹^ORCID,Li Daofeng¹^ORCID,Ni Rui²^ORCID,Zhu Jinkang³^ORCID,Zhang Sihai⁴^ORCID

Affiliation:

1. Department of Electronic Engineering and Information Science, School of Information Science and Technology, University of Science and Technology of China, Hefei, China

2. Wireless Technology Laboratory, 2012 Laboratory, Huawei Tech. Co. Ltd., Shenzhen, China

3. PCNSS Laboratory, School of Information Science and Technology, University of Science and Technology of China, Hefei, China

4. Key Laboratory of Wireless-Optical Communications, Chinese Academy of Sciences, School of Microelectronics, University of Science and Technology of China, Hefei, China

Funder

National Key R&D Program of China

Natural Science Foundation of Anhui Province

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/10235272/10053383.pdf?arnumber=10053383

Reference34 articles.

1. A Secure Federated Transfer Learning Framework

2. Federated Kernelized multi-task learning;caldas;Proc SysML Conf,0

3. Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

4. Federated Transfer Learning for Bearing Fault Diagnosis With Discrepancy-Based Weighted Federated Averaging

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Clients Eligibility-Based Lightweight Protocol in Federated Learning: An IDS Use Case;IEEE Transactions on Network and Service Management;2024-08

2. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024