There is a significant relevance of federated learning (FL) in the realm of Artificial Intelligence of Things (AIoT). However, most existing FL works do not use datasets collected from authentic IoT devices and thus do not capture unique modalities and inherent challenges of IoT data. To fill this critical gap, in this work, we introduce FedAIoT, an FL benchmark for AIoT. FedAIoT includes eight datasets collected from a wide range of IoT devices. These datasets cover unique IoT modalities and target representative applications of AIoT. FedAIoT also includes a unified end-to-end FL framework for AIoT that simplifies benchmarking the performance of the datasets. Our benchmark results shed light on the opportunities and challenges of FL for AIoT. We hope FedAIoT could serve as an invaluable resource to foster advancements in the important field of FL for AIoT. The repository of FedAIoT is maintained at https://github.com/AIoT-MLSys-Lab/FedAIoT.

翻译：联邦学习（FL）在物联网人工智能（AIoT）领域具有显著相关性。然而，现有的大多数联邦学习研究工作并未使用从真实物联网设备收集的数据集，因此未能捕捉到物联网数据特有的模态及其固有挑战。为填补这一关键空白，本研究提出了FedAIoT——一个面向AIoT的联邦学习基准。FedAIoT包含从广泛物联网设备采集的八个数据集，涵盖了独特的物联网模态并面向AIoT的代表性应用场景。FedAIoT还提供了一个统一的端到端AIoT联邦学习框架，可简化对这些数据集的性能基准测试。我们的基准测试结果揭示了AIoT联邦学习面临的机遇与挑战。我们希望FedAIoT能成为推动AIoT联邦学习这一重要领域发展的宝贵资源。FedAIoT的代码仓库维护于 https://github.com/AIoT-MLSys-Lab/FedAIoT。