PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916263346831360 |
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| author | Luqman, Alka Shekhar, Shivanshu Chattopadhyay, Anupam |
| author_facet | Luqman, Alka Shekhar, Shivanshu Chattopadhyay, Anupam |
| contents | This work integrates peer-to-peer federated learning tools with NS3, a widely used network simulator, to create a novel simulator designed to allow heterogeneous device experiments in federated learning. This cross-platform adaptability addresses a critical gap in existing simulation tools, enhancing the overall utility and user experience. NS3 is leveraged to simulate WiFi dynamics to facilitate federated learning experiments with participants that move around physically during training, leading to dynamic network characteristics. Our experiments showcase the simulator's efficiency in computational resource utilization at scale, with a maximum of 450 heterogeneous devices modelled as participants in federated learning. This positions it as a valuable tool for simulation-based investigations in peer-to-peer federated learning. The framework is open source and available for use and extension to the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale Luqman, Alka Shekhar, Shivanshu Chattopadhyay, Anupam Distributed, Parallel, and Cluster Computing Artificial Intelligence This work integrates peer-to-peer federated learning tools with NS3, a widely used network simulator, to create a novel simulator designed to allow heterogeneous device experiments in federated learning. This cross-platform adaptability addresses a critical gap in existing simulation tools, enhancing the overall utility and user experience. NS3 is leveraged to simulate WiFi dynamics to facilitate federated learning experiments with participants that move around physically during training, leading to dynamic network characteristics. Our experiments showcase the simulator's efficiency in computational resource utilization at scale, with a maximum of 450 heterogeneous devices modelled as participants in federated learning. This positions it as a valuable tool for simulation-based investigations in peer-to-peer federated learning. The framework is open source and available for use and extension to the community. |
| title | PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2405.17839 |