Over-the-air Federated Policy Gradient
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917596730753024 |
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| author | Yang, Huiwen Huang, Lingying Dey, Subhrakanti Shi, Ling |
| author_facet | Yang, Huiwen Huang, Lingying Dey, Subhrakanti Shi, Ling |
| contents | In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing. In this paper, we propose the over-the-air federated policy gradient algorithm, where all agents simultaneously broadcast an analog signal carrying local information to a common wireless channel, and a central controller uses the received aggregated waveform to update the policy parameters. We investigate the effect of noise and channel distortion on the convergence of the proposed algorithm, and establish the complexities of communication and sampling for finding an $ε$-approximate stationary point. Finally, we present some simulation results to show the effectiveness of the algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_16592 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Over-the-air Federated Policy Gradient Yang, Huiwen Huang, Lingying Dey, Subhrakanti Shi, Ling Machine Learning Distributed, Parallel, and Cluster Computing Signal Processing In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing. In this paper, we propose the over-the-air federated policy gradient algorithm, where all agents simultaneously broadcast an analog signal carrying local information to a common wireless channel, and a central controller uses the received aggregated waveform to update the policy parameters. We investigate the effect of noise and channel distortion on the convergence of the proposed algorithm, and establish the complexities of communication and sampling for finding an $ε$-approximate stationary point. Finally, we present some simulation results to show the effectiveness of the algorithm. |
| title | Over-the-air Federated Policy Gradient |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing Signal Processing |
| url | https://arxiv.org/abs/2310.16592 |