The growing integration of the Internet of Things (IoT) and Artificial Intelligence (AI), commonly referred to as the Artificial Intelligence of Things (AIoT), has amplified the importance of Federated Learning (FL). However, the application of FL in AIoT is challenged by the lack of authentic IoT datasets and the constraints associated with model-homogeneous FL approaches.Addressing these gaps, this thesis introduces two significant contributions: FedAIoT and FedRolex. FedAIoT is a comprehensive FL benchmark designed for AIoT, encompassing eight diverse datasets collected from a wide range of IoT devices. It offers a unified end-to-end FL framework, making it an invaluable tool for standardizing AIoT-based FL applications. The framework is available at https://github.com/AIoT-MLSys-Lab/FedAIoT. On the other hand, FedRolex is a novel Partial Training (PT)-based model-heterogeneous FL approach. With an emphasis on device heterogeneity typical in AIoT applications, FedRolex enables the training of a global server model that is larger than any client model, by using a rolling sub-model extraction scheme. This approach mitigates client drift, enhances the performance of low-end devices, and reduces the gap between model-heterogeneous and model-homogeneous FL. Benchmark results indicate that FedRolex outperforms existing PT-based model-heterogeneous FL methods, making it a crucial resource for researchers and practitioners in the field of FL for AIoT. Our code is available at: https://github.com/AIoT-MLSys-Lab/FedRolex.
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