Model Compression by Count Sketch for Over-the-Air Stateless Federated Learning
Author:
Affiliation:
1. Department of Information and Communication Engineering, Myongji University, Yongin, Gyeonggi, Republic of Korea
2. IT Media Engineering Program, Seoul National University of Science and Technology, Seoul, Republic of Korea
Funder
Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korea Government
2022 Research Fund of Myongji University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6488907/10551641/10472332.pdf?arnumber=10472332
Reference49 articles.
1. Gradient Compression via Count-Sketch for Analog Federated Learning
2. Wireless Network Intelligence at the Edge
3. Communication-efficient learning of deep networks from decentralized data;McMahan
4. Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning
5. Federated multi-task learning;Smith
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1. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024
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