mlsanityData to 7 Oct 2026

Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks

arXiv 2108.02517 ↗First posted 5 Aug 2021cs.IT cs.AI cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) has evolved as a promising technique to handle distributed machine learning across edge devices. A single neural network (NN) that optimises a global objective is generally learned in most work in FL, which could be suboptimal for edge devices. Although works finding a NN personalised for edge device specific tasks exist, they lack generalisation and/or convergence guarantees. In this paper, a novel communication efficient FL algorithm for personalised learning in a wireless setting with guarantees is presented. The algorithm relies on finding a ``better`` empirical estimate of losses at each device, using a weighted average of the losses across different devices. It is devised from a Probably Approximately Correct (PAC) bound on the true loss in terms of the proposed empirical loss and is bounded by (i) the Rademacher complexity, (ii) the discrepancy, (iii) and a penalty term. Using a signed gradient feedback to find a personalised NN at each device, it is also proven to converge in a Rayleigh flat fading (in the uplink) channel, at a rate of the order max{1/SNR,1/sqrt(T)} Experimental results show that the proposed algorithm outperforms locally trained devices as well as the conventionally used FedAvg and FedSGD algorithms under practical SNR regimes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 43 references that are in the map. First posted on arXiv 5 Aug 2021.

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32 of 43 references.

References · 32

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20 more
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