Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 20, Issue 3, Pages 1990-2004Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.3038407
Keywords
Federated learning; distributed stochastic gradient descent; massive multiple-input multiple-output (MIMO); compressive sensing; multi-antenna technique
Funding
- National Research Foundation of Korea (NRF) [NRF-2020R1G1A1099962]
- Institute of Information and Communications Technology Planning and Evaluation (IITP) - Korean Government (MSIT) [2016-0-00123]
- National Natural Science Foundation of China [61872184]
- U.S. National Science Foundation [CCF-0939370, CCF-1908308]
- National Research Foundation of Korea [4199990114297] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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This paper introduces a federated learning approach using compressive sensing for massive multiple-input multiple-output communication systems, aiming to accurately reconstruct local gradient vectors computed-and-sent from wireless devices at the central server. The proposed algorithm iteratively finds the linear minimum-mean-square-error estimate of transmitted signals with sparsity, reducing computational complexity compared to traditional approaches and narrowing the performance gap between federated learning and centralized learning.
Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. In this paper, we present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems in which the central server equipped with a massive antenna array communicates with the wireless devices. One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices. To overcome this challenge, we first establish a transmission strategy to construct sparse transmitted signals from the local gradient vectors at the devices. We then propose a compressive sensing algorithm enabling the server to iteratively find the linear minimum-mean-square-error (LMMSE) estimate of the transmitted signal by exploiting its sparsity. We also derive an analytical threshold for the residual error at each iteration, to design the stopping criterion of the proposed algorithm. We show that for a sparse transmitted signal, the proposed algorithm requires less computationally complexity than LMMSE. Simulation results demonstrate that the presented approach outperforms conventional linear beamforming approaches and reduces the performance gap between federated learning and centralized learning with perfect reconstruction.
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