Federated Learning via Lattice Joint Source-Channel Coding
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929262463811584 |
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| author | Azimi-Abarghouyi, Seyed Mohammad Varshney, Lav R. |
| author_facet | Azimi-Abarghouyi, Seyed Mohammad Varshney, Lav R. |
| contents | This paper introduces a universal federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. A novel two-layer receiver structure at the server is designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. Numerical experiments validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme markedly surpasses other over-the-air FL strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01023 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Learning via Lattice Joint Source-Channel Coding Azimi-Abarghouyi, Seyed Mohammad Varshney, Lav R. Information Theory Machine Learning This paper introduces a universal federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. A novel two-layer receiver structure at the server is designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. Numerical experiments validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme markedly surpasses other over-the-air FL strategies. |
| title | Federated Learning via Lattice Joint Source-Channel Coding |
| topic | Information Theory Machine Learning |
| url | https://arxiv.org/abs/2403.01023 |