FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916364710576128 |
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| author | Alam, Samiul Zhang, Tuo Feng, Tiantian Shen, Hui Cao, Zhichao Zhao, Dong Ko, JeongGil Somasundaram, Kiran Narayanan, Shrikanth S. Avestimehr, Salman Zhang, Mi |
| author_facet | Alam, Samiul Zhang, Tuo Feng, Tiantian Shen, Hui Cao, Zhichao Zhao, Dong Ko, JeongGil Somasundaram, Kiran Narayanan, Shrikanth S. Avestimehr, Salman Zhang, Mi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00109 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things Alam, Samiul Zhang, Tuo Feng, Tiantian Shen, Hui Cao, Zhichao Zhao, Dong Ko, JeongGil Somasundaram, Kiran Narayanan, Shrikanth S. Avestimehr, Salman Zhang, Mi Machine Learning Distributed, Parallel, and Cluster Computing Digital Libraries 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. |
| title | FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Things |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing Digital Libraries |
| url | https://arxiv.org/abs/2310.00109 |