CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface
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arXiv
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866909365258158080 |
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| author | Liu, Hang Yuan, Xiaojun Zhang, Ying-Jun Angela |
| author_facet | Liu, Hang Yuan, Xiaojun Zhang, Ying-Jun Angela |
| contents | We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable. We leverage the reconfigurable intelligent surface (RIS) technology to align the cascaded channel coefficients for CSIT-free model aggregation. To this end, we jointly optimize the RIS and the receiver by minimizing the aggregation error under the channel alignment constraint. We then develop a difference-of-convex algorithm for the resulting non-convex optimization. Numerical experiments on image classification show that the proposed method is able to achieve a similar learning accuracy as the state-of-the-art CSIT-based solution, demonstrating the efficiency of our approach in combating the lack of CSIT. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2102_10749 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface Liu, Hang Yuan, Xiaojun Zhang, Ying-Jun Angela Information Theory Machine Learning Networking and Internet Architecture Signal Processing We study over-the-air model aggregation in federated edge learning (FEEL) systems, where channel state information at the transmitters (CSIT) is assumed to be unavailable. We leverage the reconfigurable intelligent surface (RIS) technology to align the cascaded channel coefficients for CSIT-free model aggregation. To this end, we jointly optimize the RIS and the receiver by minimizing the aggregation error under the channel alignment constraint. We then develop a difference-of-convex algorithm for the resulting non-convex optimization. Numerical experiments on image classification show that the proposed method is able to achieve a similar learning accuracy as the state-of-the-art CSIT-based solution, demonstrating the efficiency of our approach in combating the lack of CSIT. |
| title | CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface |
| topic | Information Theory Machine Learning Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2102.10749 |