Random Orthogonalization for Federated Learning in Massive MIMO Systems

Author:

Wei Xizixiang1,Shen Cong1,Yang Jing2,Poor H. Vincent3

Affiliation:

1. University of Virginia,Department of Electrical and Computer Engineering,USA

2. The Pennsylvania State University,Department of Electrical Engineering,USA

3. Princeton University,Department of Electrical and Computer Engineering,USA

Funder

National Science Foundation

Publisher

IEEE

Reference18 articles.

1. Gradient estimation for federated learning over massive MIMO communication systems;jeon,2020

2. LTE - The UMTS Long Term Evolution

3. Aspects of favorable propagation in massive MIMO;ngo;Proc 22nd European Signal Processing Conference (EUSIPCO),2014

4. Local SGD converges fast and communicates little;stich;Proc International Conference on Learning Representations (ICLR),2018

5. On the convergence of FedAvg on non-IID data;li;Proc International Conference on Learning Representations (ICLR),2020

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3. Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification;2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC);2022-07-04

4. Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations;IEEE Open Journal of the Communications Society;2022

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