Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due to the inherent data and device heterogeneity issues, existing FL-based AIoT systems suffer from the model selection problem. Although various heterogeneous FL methods have been investigated to enable collaborative training among heterogeneous models, there is still a lack of i) wise heterogeneous model generation methods for devices, ii) consideration of uncertain factors, and iii) performance guarantee for large models, thus strongly limiting the overall FL performance. To address the above issues, this paper introduces a novel heterogeneous FL framework named FlexFL. By adopting our Average Percentage of Zeros (APoZ)-guided flexible pruning strategy, FlexFL can effectively derive best-fit models for heterogeneous devices to explore their greatest potential. Meanwhile, our proposed adaptive local pruning strategy allows AIoT devices to prune their received models according to their varying resources within uncertain scenarios. Moreover, based on self-knowledge distillation, FlexFL can enhance the inference performance of large models by learning knowledge from small models. Comprehensive experimental results show that, compared to state-of-the-art heterogeneous FL methods, FlexFL can significantly improve the overall inference accuracy by up to 14.24%.

翻译：随着深度学习（DL）技术的日益普及，越来越多的物联网人工智能（AIoT）系统采用联邦学习（FL）来实现AIoT设备间隐私保护的协同学习。然而，由于固有的数据和设备异构性问题，现有基于FL的AIoT系统面临模型选择难题。尽管已有多种异构FL方法被研究以实现异构模型间的协同训练，但仍缺乏：i) 面向设备的智能异构模型生成方法，ii) 对不确定因素的考量，以及iii) 对大模型的性能保障，从而严重限制了整体FL性能。为解决上述问题，本文提出了一种名为FlexFL的新型异构FL框架。通过采用我们提出的基于平均零值比例（APoZ）引导的灵活剪枝策略，FlexFL能够有效为异构设备推导出最适配的模型，以挖掘其最大潜力。同时，我们提出的自适应局部剪枝策略允许AIoT设备在不确定场景下根据其变化的资源状况对接收到的模型进行剪枝。此外，基于自知识蒸馏，FlexFL能够通过学习小模型的知识来提升大模型的推理性能。综合实验结果表明，与最先进的异构FL方法相比，FlexFL能将整体推理精度最高提升14.24%。