Federated Edge Learning with Misaligned Over-The-Air Computation

• URL: http://arxiv.org/abs/2102.13604v1
• Date: Fri, 26 Feb 2021 17:19:56 GMT
• Title: Federated Edge Learning with Misaligned Over-The-Air Computation
• Authors: Yulin Shao, Deniz Gunduz, Soung Chang Liew
• Abstract summary: Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning. How to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. This paper formulates the problem of misaligned OAC for federated edge learning and puts forth a whitened matched filtering and sampling scheme.
• Score: 36.39188653838991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning. OAC, however, hinges on accurate channel-gain precoding and strict synchronization among the edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for federated edge learning and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent, samples from the misaligned and overlapped signals. Given the whitened samples, a sum-product ML estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. In particular, the computational complexity of our sum-product ML estimator is linear in the packet length and hence is significantly lower than the conventional ML estimator. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is non-severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement in the estimation process. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.

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