FedACT: An adaptive chained training approach for federated learning in computing power networks
Federated Learning (FL) is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security. However, the traditional FL model in communication scenarios, whether for uplink or downlink communications, may give rise to several...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-12-01
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| Series: | Digital Communications and Networks |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864823001839 |
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| _version_ | 1846101618912657408 |
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| author | Min Wei Qianying Zhao Bo Lei Yizhuo Cai Yushun Zhang Xing Zhang Wenbo Wang |
| author_facet | Min Wei Qianying Zhao Bo Lei Yizhuo Cai Yushun Zhang Xing Zhang Wenbo Wang |
| author_sort | Min Wei |
| collection | DOAJ |
| description | Federated Learning (FL) is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security. However, the traditional FL model in communication scenarios, whether for uplink or downlink communications, may give rise to several network problems, such as bandwidth occupation, additional network latency, and bandwidth fragmentation. In this paper, we propose an adaptive chained training approach (FedACT) for FL in computing power networks. First, a Computation-driven Clustering Strategy (CCS) is designed. The server clusters clients by task processing delays to minimize waiting delays at the central server. Second, we propose a Genetic-Algorithm-based Sorting (GAS) method to optimize the order of clients participating in training. Finally, based on the table lookup and forwarding rules of the Segment Routing over IPv6 (SRv6) protocol, the sorting results of GAS are written into the SRv6 packet header, to control the order in which clients participate in model training. We conduct extensive experiments on two datasets of CIFAR-10 and MNIST, and the results demonstrate that the proposed algorithm offers improved accuracy, diminished communication costs, and reduced network delays. |
| format | Article |
| id | doaj-art-43ea1bb01fde44e1a29aaa0286805f7c |
| institution | Kabale University |
| issn | 2352-8648 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj-art-43ea1bb01fde44e1a29aaa0286805f7c2024-12-29T04:47:36ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482024-12-0110615761589FedACT: An adaptive chained training approach for federated learning in computing power networksMin Wei0Qianying Zhao1Bo Lei2Yizhuo Cai3Yushun Zhang4Xing Zhang5Wenbo Wang6China Telecom Research Institute, Beijing 102209, China; Corresponding author.China Telecom Research Institute, Beijing 102209, ChinaChina Telecom Research Institute, Beijing 102209, ChinaWireless Signal Processing and Network Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWireless Signal Processing and Network Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWireless Signal Processing and Network Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaWireless Signal Processing and Network Laboratory, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaFederated Learning (FL) is a novel distributed machine learning methodology that addresses large-scale parallel computing challenges while safeguarding data security. However, the traditional FL model in communication scenarios, whether for uplink or downlink communications, may give rise to several network problems, such as bandwidth occupation, additional network latency, and bandwidth fragmentation. In this paper, we propose an adaptive chained training approach (FedACT) for FL in computing power networks. First, a Computation-driven Clustering Strategy (CCS) is designed. The server clusters clients by task processing delays to minimize waiting delays at the central server. Second, we propose a Genetic-Algorithm-based Sorting (GAS) method to optimize the order of clients participating in training. Finally, based on the table lookup and forwarding rules of the Segment Routing over IPv6 (SRv6) protocol, the sorting results of GAS are written into the SRv6 packet header, to control the order in which clients participate in model training. We conduct extensive experiments on two datasets of CIFAR-10 and MNIST, and the results demonstrate that the proposed algorithm offers improved accuracy, diminished communication costs, and reduced network delays.http://www.sciencedirect.com/science/article/pii/S2352864823001839Computing power network (CPN)Federated learning (FL)Segment routing IPv6 (SRv6)Communication overheadsModel accuracy |
| spellingShingle | Min Wei Qianying Zhao Bo Lei Yizhuo Cai Yushun Zhang Xing Zhang Wenbo Wang FedACT: An adaptive chained training approach for federated learning in computing power networks Digital Communications and Networks Computing power network (CPN) Federated learning (FL) Segment routing IPv6 (SRv6) Communication overheads Model accuracy |
| title | FedACT: An adaptive chained training approach for federated learning in computing power networks |
| title_full | FedACT: An adaptive chained training approach for federated learning in computing power networks |
| title_fullStr | FedACT: An adaptive chained training approach for federated learning in computing power networks |
| title_full_unstemmed | FedACT: An adaptive chained training approach for federated learning in computing power networks |
| title_short | FedACT: An adaptive chained training approach for federated learning in computing power networks |
| title_sort | fedact an adaptive chained training approach for federated learning in computing power networks |
| topic | Computing power network (CPN) Federated learning (FL) Segment routing IPv6 (SRv6) Communication overheads Model accuracy |
| url | http://www.sciencedirect.com/science/article/pii/S2352864823001839 |
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