There is a significant relevance of federated learning (FL) in the realm of Artificial Intelligence of Things (AIoT). However, most existing FL works are not conducted on datasets collected from authentic IoT devices that capture unique modalities and inherent challenges of IoT data. In this work, we introduce FedAIoT, an FL benchmark for AIoT to fill this critical gap. FedAIoT includes eight datatsets 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）在人工智能物联网（AIoT）领域具有重要相关性。然而，现有大多数FL研究并非基于真实物联网设备采集的数据集，这些数据集能够捕捉物联网数据的独特模态及其固有挑战。本文提出FedAIoT——面向AIoT的FL基准，旨在填补这一关键空白。FedAIoT涵盖从广泛物联网设备收集的八个数据集，这些数据集覆盖了物联网的独特模态，并针对AIoT的代表性应用场景。同时，FedAIoT包含一个面向AIoT的统一端到端FL框架，简化了数据集的性能基准测试。我们的基准测试结果揭示了FL在AIoT领域的机遇与挑战。我们希望FedAIoT能成为推动这一重要领域发展的宝贵资源。FedAIoT的代码仓库托管于https://github.com/AIoT-MLSys-Lab/FedAIoT。