AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems

arXiv:2311.13166v2 Announce Type: replace
Abstract: Although Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices, it suffers from the problem of low classification performance due to various heterogeneity factors (e.g., computing capacity, memory size) of devices and uncertain operating environments. To address these issues, this paper introduces an effective FL approach named AdaptiveFL based on a novel fine-grained width-wise model pruning strategy, which can generate various heterogeneous local models for heterogeneous AIoT devices. …

abstract aiot artificial artificial intelligence arxiv capacity classification collaborative computing cs.dc cs.lg devices environments federated learning intelligence low memory performance systems type uncertain