Leveraging Semi-Connected Devices To Enhance Federated Learning

Abstract

Federated Learning (FL) was introduced to over-come traditional Machine Learning data privacy concerns, and thus, enable us to gain access to more data. Data owners, clients, are orchestrated by a central FL-server to train data locally and only share their model weights. FL approaches have mainly relied on Cloud and/or Edge to aggregate these model weights and propagate training knowledge across clients. However, several issues hinder the scalability of these approaches, especially in communication-challenged environments. In this paper, we propose a novel semi-distributed system to improve FL training accuracy and time, as well as resource-efficiency at the clients. We leverage co-located clusters of high-end IoT devices, known as FemtoClouds, to propagate training knowledge beyond the Edge. We only leverage Edge/Cloud opportunistically to prop-agate knowledge across FemtoCloud pools. Our evaluation shows that our semi-distributed FemtoClouds system achieves competitive accuracy to state-of-the-art FL approaches, with up to 95% time savings and up to 84% energy savings.