Communication-Efficient Federated Learning Over MIMO Multiple Access Channels

نویسندگان

چکیده

Communication efficiency is of importance for wireless federated learning systems. In this paper, we propose a communication-efficient strategy over multiple-input multiple-output (MIMO) multiple access channels (MACs). The proposed comprises two components. When sending locally computed gradient, each device compresses high dimensional local gradient to lower-dimensional vectors using block sparsification. receiving superposition the compressed gradients via MIMO-MAC, parameter server (PS) performs joint MIMO detection and sparse local-gradient recovery. Inspired by turbo decoding principle, our detection-and-recovery algorithm accurately recovers high-dimensional iteratively exchanging their beliefs recovery outputs. We then analyze reconstruction error its impact on convergence rate learning. From simulations, compression methods diminish communication cost significantly while achieving identical classification accuracy case without any compression.

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ژورنال

عنوان ژورنال: IEEE Transactions on Communications

سال: 2022

ISSN: ['1558-0857', '0090-6778']

DOI: https://doi.org/10.1109/tcomm.2022.3198433