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Approximate Wireless Communication for Federated Learning by Ma, Xiang; Sun, Haijian; Hu, Rose Qingyang; Qian, Yi is a scholarly article available to read on EtoBox.

What is Approximate Wireless Communication for Federated Learning about?

This paper presents an approximate wireless communication scheme for federated learning (FL) model aggregation in the uplink transmission. We consider a realistic channel that reveals bit errors during FL model exchange in wireless networks. Our study demonstrates that random bit errors during model transmission can significantly affect FL performance. To overcome this challenge, we propose an approximate communication scheme based on the mathematical and statistical proof that machine learning (ML) model gradients are bounded under certain constraints. This bound enables us to introduce a novel encoding scheme for float-to-binary representation of gradient values and their QAM constellation mapping. Besides, since FL gradients are error-resilient, the proposed scheme simply delivers gradients with errors when the channel quality is satisfactory, eliminating extensive error-correcting codes and/or retransmission. The direct benefits include less overhead and lower latency. The proposed scheme is well-suited for resource-constrained devices in wireless networks. Through simulations, we show that the proposed scheme is effective in reducing the impact of bit errors on FL performance

Author

Ma, Xiang; Sun, Haijian; Hu, Rose Qingyang; Qian, Yi

Published

2023

Language

EN