Resilient and Communication Efficient Learning for Heterogeneous Federated Systems.

ABSTRACT: The rise of Federated Learning (FL) is bringing machine learning to edge computing by utilizing data scattered across edge devices. However, the heterogeneity of edge network topologies and the uncertainty of wireless transmission are two major obstructions of FL's wide application in edge computing, leading to prohibitive convergence time and high communication cost. In this work, we propose an FL scheme to address both challenges simultaneously. Specifically, we enable edge devices to learn self-distilled neural networks that are readily prunable to arbitrary sizes, which capture the knowledge of the learning domain in a nested and progressive manner. Not only does our approach tackle system heterogeneity by serving edge devices with varying model architectures, but it also alleviates th

SUBMITTER: Zhu Z 

PROVIDER: S-EPMC10097502 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature