Efficient Model Compression via Global Sparsification for Over-the-Air Federated Learning
Author:
Affiliation:
1. Shenzhen International Graduate School, Tsinghua University,Shenzhen,China
2. Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing,Guilin,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10464388/10464393/10464512.pdf?arnumber=10464512
Reference14 articles.
1. Communication-efficient learning of deep networks from decentralized data;McMahan,2017
2. Federated Learning in Mobile Edge Networks: A Comprehensive Survey
3. Design and Analysis of Uplink and Downlink Communications for Federated Learning
4. Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning
5. Broadband Analog Aggregation for Low-Latency Federated Edge Learning
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