Quantized Federated Learning under Transmission Delay and Outage Constraints

• URL: http://arxiv.org/abs/2106.09397v1
• Date: Thu, 17 Jun 2021 11:29:12 GMT
• Title: Quantized Federated Learning under Transmission Delay and Outage Constraints
• Authors: Yanmeng Wang, Yanqing Xu, Qingjiang Shi, Tsung-Hui Chang
• Abstract summary: Federated learning is a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge. In practical systems with limited radio resources, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO) We propose a robust FL scheme, named FedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability.
• Score: 30.892724364965005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to expedite the FL process, most of them have assumed ideal wireless channels which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, in practical systems with limited radio resources such as constraint on the training latency and constraints on the transmission power and bandwidth, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO). In this paper, we consider such non-ideal wireless channels, and carry out the first analysis showing that the FL convergence can be severely jeopardized by TO and QE, but intriguingly can be alleviated if the clients have uniform outage probabilities. These insightful results motivate us to propose a robust FL scheme, named FedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability. Extensive experimental results are presented to show the superior performance of FedTOE for a deep learning-based classification task with transmission latency constraints.

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