FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems
Fuente:
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_ | 1866915001980157952 |
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| author | Woisetschläger, Herbert Erben, Alexander Mayer, Ruben Wang, Shiqiang Jacobsen, Hans-Arno |
| author_facet | Woisetschläger, Herbert Erben, Alexander Mayer, Ruben Wang, Shiqiang Jacobsen, Hans-Arno |
| contents | Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. Heterogeneous hardware, unreliable client devices, and energy constraints often characterize edge computing systems. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities. We focus on computational and communication bottlenecks, client behavior, and data security implications. Our experiments with models varying from 14K to 80M trainable parameters are carried out on dedicated hardware with emulated network characteristics and client behavior. We find that state-of-the-art embedded hardware has significant memory bottlenecks, leading to 4x longer processing times than on modern data center GPUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_05172 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems Woisetschläger, Herbert Erben, Alexander Mayer, Ruben Wang, Shiqiang Jacobsen, Hans-Arno Machine Learning Distributed, Parallel, and Cluster Computing I.2.11; C.2.4; C.4; D.2.8 Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. Heterogeneous hardware, unreliable client devices, and energy constraints often characterize edge computing systems. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities. We focus on computational and communication bottlenecks, client behavior, and data security implications. Our experiments with models varying from 14K to 80M trainable parameters are carried out on dedicated hardware with emulated network characteristics and client behavior. We find that state-of-the-art embedded hardware has significant memory bottlenecks, leading to 4x longer processing times than on modern data center GPUs. |
| title | FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing I.2.11; C.2.4; C.4; D.2.8 |
| url | https://arxiv.org/abs/2306.05172 |