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 S. Narayanan Salman Avestimehr Mi Zhang
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.