FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things

Samiul Alam, Tuo Zhang, Tiantian Feng, Hui Shen, Zhichao Cao, Dong Zhao, Jeonggil Ko, Kiran Somasundaram, Shrikanth Narayanan, Salman Avestimehr, Mi Zhang

Primary Area: datasets and benchmarks

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Keywords: AIoT, Federated Learning

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TL;DR: Unified framework for benchmarking federated learning in AIoT, providing diverse datasets, tasks and analyses.

Abstract: There is a significant relevance of federated learning (FL) in the realm of Artificial Intelligence of Things (AIoT). However, most of 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, a FL benchmark for AIoT to fill this critical gap. FedAIoT includes eight well-chosen datatsets collected from a wide range of IoT devices. These datasets cover unique IoT modalities and target representative applications of AIoT. In addition, FedAIoT 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 that FedAIoT could serve as an invaluable resource for researchers and practitioners to foster advancements in the important field of FL for AIoT.

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Submission Number: 4689