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.

适应性模型修剪策略的自适应联邦学习方法针对人工智能物联网设备中的异构性因素进行合作学习，通过强化学习的设备选择机制，将异构模型动态地分发给相应的设备进行本地训练，从而实现推断性能的提升。