Accelerating Federated Learning with a Global Biased Optimiser

• URL: http://arxiv.org/abs/2108.09134v1
• Date: Fri, 20 Aug 2021 12:08:44 GMT
• Title: Accelerating Federated Learning with a Global Biased Optimiser
• Authors: Jed Mills, Jia Hu, Geyong Min, Rui Jin, Siwei Zheng, Jin Wang
• Abstract summary: Federated Learning (FL) is a recent development in the field of machine learning that collaboratively trains models without the training data leaving client devices. We propose a novel, generalised approach for applying adaptive optimisation techniques to FL with the Federated Global Biased Optimiser (FedGBO) algorithm. FedGBO accelerates FL by applying a set of global biased optimiser values during the local training phase of FL, which helps to reduce client-drift' from non-IID data.
• Score: 16.69005478209394
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is a recent development in the field of machine learning that collaboratively trains models without the training data leaving client devices, in order to preserve data-privacy. In realistic settings, the total training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, which has been shown extensively to harm FL convergence speed and final model performance. We propose a novel, generalised approach for applying adaptive optimisation techniques to FL with the Federated Global Biased Optimiser (FedGBO) algorithm. FedGBO accelerates FL by applying a set of global biased optimiser values during the local training phase of FL, which helps to reduce `client-drift' from non-IID data, whilst also benefiting from adaptive momentum/learning-rate methods. We show that the FedGBO update with a generic optimiser can be viewed as a centralised update with biased gradients and optimiser update, and use this theoretical framework to prove the convergence of FedGBO using momentum-Stochastic Gradient Descent. We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL.

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