Optimizing Parameter Mixing Under Constrained Communications in Parallel Federated Learning-Reference-Cited by-同舟云学术

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Optimizing Parameter Mixing Under Constrained Communications in Parallel Federated Learning

Published:2023-12 Issue:6 Volume:31 Page:2640-2652
ISSN:1063-6692
Container-title:IEEE/ACM Transactions on Networking
language:
Short-container-title:IEEE/ACM Trans. Networking

Author:

Liu Xuezheng¹^ORCID,Yan Zirui²^ORCID,Zhou Yipeng³^ORCID,Wu Di¹^ORCID,Chen Xu¹^ORCID,Wang Jessie Hui⁴^ORCID

Affiliation:

1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

2. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA

3. Department of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia

4. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/90/10365667/10082940.pdf?arnumber=10082940

Reference57 articles.

1. Practical secure aggregation for federated learning on user-held data;Bonawitz

2. How to backdoor federated learning;Bagdasaryan

3. Differentially private federated learning: A client level perspective;Geyer;arXiv:1712.07557,2017

4. Protection against reconstruction and its applications in private federated learning;Bhowmick;arXiv:1812.00984,2018

5. Towards federated learning at scale: System design;Bonawitz

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