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FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things

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arxiv 2310.00109 v3 pith:UHOCY3UY submitted 2023-09-29 cs.LG cs.DCcs.DL

classification cs.LGcs.DCcs.DL
keywords fedaiotaiotdatasetsbenchmarkartificialchallengescollecteddevices
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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