Optimal Batch Allocation for Wireless Federated Learning
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929301807431680 |
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| author | Song, Jaeyoung Jeon, Sang-Woon |
| author_facet | Song, Jaeyoung Jeon, Sang-Woon |
| contents | Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze the number of iterations required for federated learning to reach a specific optimality gap from a minimum global loss. Subsequently, we characterize the time required for each iteration under two fundamental multiple access schemes: time-division multiple access (TDMA) and random access (RA). We propose a step-wise batch allocation, demonstrated to be optimal for TDMA-based federated learning systems. Additionally, we show that the non-zero batch gap between devices provided by the proposed step-wise batch allocation significantly reduces the completion time for RA-based learning systems. Numerical evaluations validate these analytical results through real-data experiments, highlighting the remarkable potential for substantial completion time reduction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02395 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimal Batch Allocation for Wireless Federated Learning Song, Jaeyoung Jeon, Sang-Woon Machine Learning Distributed, Parallel, and Cluster Computing Federated learning aims to construct a global model that fits the dataset distributed across local devices without direct access to private data, leveraging communication between a server and the local devices. In the context of a practical communication scheme, we study the completion time required to achieve a target performance. Specifically, we analyze the number of iterations required for federated learning to reach a specific optimality gap from a minimum global loss. Subsequently, we characterize the time required for each iteration under two fundamental multiple access schemes: time-division multiple access (TDMA) and random access (RA). We propose a step-wise batch allocation, demonstrated to be optimal for TDMA-based federated learning systems. Additionally, we show that the non-zero batch gap between devices provided by the proposed step-wise batch allocation significantly reduces the completion time for RA-based learning systems. Numerical evaluations validate these analytical results through real-data experiments, highlighting the remarkable potential for substantial completion time reduction. |
| title | Optimal Batch Allocation for Wireless Federated Learning |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2404.02395 |