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

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 { }. Index Terms Federated Learning, Multi-Task-Learning, SignSGD, Deep Learning, PAC bound, Distributed ML. The authors are with the Department of electrical engineering at IIT Dharwad, Dharwad, Karnataka. The wide spread adoption of smartphones and internet services with considerable computing capabilities has enabled machine learning (ML) algorithms to work in a distributed fashion (see [1]).