FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
arxiv(2023)
Abstract
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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