AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
arxiv(2023)
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. By using our proposed reinforcement
learning-based device selection mechanism, AdaptiveFL can adaptively dispatch
suitable heterogeneous models to corresponding AIoT devices on the fly based on
their available resources for local training. Experimental results show that,
compared to state-of-the-art methods, AdaptiveFL can achieve up to 16.83
inference improvements for both IID and non-IID scenarios.
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