2021
DOI: 10.48550/arxiv.2102.13604
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Federated Edge Learning with Misaligned Over-The-Air Computation

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 lear… Show more

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Cited by 1 publication
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References 34 publications
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“…While over-the-air FEEL requires accurate channel state information (CSI), it is shown in [41] that multiple antennas can help to relax the CSI requirement. The impact of imperfect CSI or synchronization across devices is considered in [42], and a digital realization of over-the-air FEEL is further proposed in [43], based on one-bit gradient quantization and majority voting.…”
mentioning
confidence: 99%
“…While over-the-air FEEL requires accurate channel state information (CSI), it is shown in [41] that multiple antennas can help to relax the CSI requirement. The impact of imperfect CSI or synchronization across devices is considered in [42], and a digital realization of over-the-air FEEL is further proposed in [43], based on one-bit gradient quantization and majority voting.…”
mentioning
confidence: 99%