Cloud-RAN Over-the-Air Federated Learning

To address limited server coverage in traditional over-the-air federated learning (OA-FL), we introduce the framework of multiple input multiple output (MIMO) cloud radio access network (Cloud-RAN) OA-FL (MIMOCROF). This framework involves a two-step model aggregation method in each training round....

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Bibliographic Details
Published in:IEEE International Conference on Communications (2003) pp. 4257 - 4262
Main Authors: Ma, Haoming, Yuan, Xiaojun, Ding, Zhi
Format: Conference Proceeding
Language:English
Published: IEEE 09.06.2024
Subjects:
Aggregates
Benchmark testing
cloud radio access network
Federated learning
lossy distributed source coding
over-the-air computation
Rate-distortion
Source coding
Training
Wireless networks
ISSN:1938-1883
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Summary:To address limited server coverage in traditional over-the-air federated learning (OA-FL), we introduce the framework of multiple input multiple output (MIMO) cloud radio access network (Cloud-RAN) OA-FL (MIMOCROF). This framework involves a two-step model aggregation method in each training round. Firstly, each base station (BS) aggregates the local updates from its served devices, resulting in an edge update. Secondly, the cloud server (CS) aggregates the edge updates from the BSs. By modeling the second step as a lossy distributed source coding (L-DSC) process, we analyze the performance of MIMOCROF from the perspective of rate-distortion theory, resulting in a unified communication-learning design approach. In the proposed design, we jointly optimize rate resources and beamforming vectors, thereby leveraging the correlation inherent in FL to achieve performance gains. Numerical results show that, by solving the optimization problem, MIMOCROF performs comparably to the error-free bound and significantly outperforms other benchmark schemes.
ISSN:1938-1883
DOI:10.1109/ICC51166.2024.10622953