Auction-based Federated Learning using Software-defined Networking for resource efficiency

Abstract

The training of global models using federated learning (FL) strategies is complicated by variations in local model quality arising from variation in data distribution across individual clients. A wide range of training strategies could be created by varying the size and distribution of the training data and the number of training iterations to be performed. All these variables affect both model quality and resource consumption. To facilitate the selection of good training strategies, we propose an auction-based FL method that can identify a training strategy that is optimal in terms of resource management efficiency subject to a given model quality requirement. An auction method is used to dynamically select resource-efficient FL clients and local models to minimize resource usage. This is enabled by using Software-defined Networking (SDN) to support the dynamic management of FL clients. We show that resource-optimal FL strategies can be implemented in the cloud/edge services market; dynamic quality-based model selection can reduce resource costs by up to 17% from the FL server's perspective. Moreover, the client utility function presented herein helps FL clients adopt practical trading strategies to cooperate efficiently with FL servers.