Hermes: Fast Semi-Asynchronous Federated Learning in LEO Constellations
Recent advances in space technology have prompted the emergence of numerous Low Earth Orbit (LEO) satellites, producing mega-constellations that can pro-vide global network coverage and collect massive distributed data, bringing new opportunities for intelligence applications to remote areas. To ach...
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| Vydané v: | IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC s. 1 - 6 |
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| Hlavní autori: | , , , , , |
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IEEE
21.04.2024
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| ISSN: | 1558-2612 |
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| Abstract | Recent advances in space technology have prompted the emergence of numerous Low Earth Orbit (LEO) satellites, producing mega-constellations that can pro-vide global network coverage and collect massive distributed data, bringing new opportunities for intelligence applications to remote areas. To achieve such goals, Federated Learning (FL) is a promising solution to train the global model over LEO satellites and ground station networks while reducing the high communication cost caused by data exchanging. However, the widely used synchronous FL may face intol-erable waiting time due to the intermittent connectivity. In addition, existing asynchronous FL suffers from model staleness attributed to asynchronous training, which may decrease the performance of the global model. To this end, we propose a novel semi-asynchronous federated learning frame-work in LEO constellations, namely Hermes, that includes a latency-aware model delivery mechanism and an adaptive semi-asynchronous aggregation algorithm to improve the convergence rate and generality of the global model. Our simulation results show that Hermes outperforms existing LEO-based FL methods across various constellation scales, achieving higher model accuracy with average speedups of 3.29x and 7.26x for MNIST and EMNIST, respectively. |
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| AbstractList | Recent advances in space technology have prompted the emergence of numerous Low Earth Orbit (LEO) satellites, producing mega-constellations that can pro-vide global network coverage and collect massive distributed data, bringing new opportunities for intelligence applications to remote areas. To achieve such goals, Federated Learning (FL) is a promising solution to train the global model over LEO satellites and ground station networks while reducing the high communication cost caused by data exchanging. However, the widely used synchronous FL may face intol-erable waiting time due to the intermittent connectivity. In addition, existing asynchronous FL suffers from model staleness attributed to asynchronous training, which may decrease the performance of the global model. To this end, we propose a novel semi-asynchronous federated learning frame-work in LEO constellations, namely Hermes, that includes a latency-aware model delivery mechanism and an adaptive semi-asynchronous aggregation algorithm to improve the convergence rate and generality of the global model. Our simulation results show that Hermes outperforms existing LEO-based FL methods across various constellation scales, achieving higher model accuracy with average speedups of 3.29x and 7.26x for MNIST and EMNIST, respectively. |
| Author | Du, Bowen Zhao, Jiejie Chen, Yan Liu, Jun Jiang, Guanjun Wang, Haiquan |
| Author_xml | – sequence: 1 givenname: Yan surname: Chen fullname: Chen, Yan email: juneliu@tsinghua.edu.cn organization: School of Software, Beihang University,Beijing,China,100191 – sequence: 2 givenname: Jun surname: Liu fullname: Liu, Jun email: zhaojiejie@zgclab.edu.cn organization: Institute for Network Sciences and Cyberspace, Tsinghua University,Beijing,China,100084 – sequence: 3 givenname: Jiejie surname: Zhao fullname: Zhao, Jiejie email: chenyan2022@buaa.edu.cn organization: Zhongguancun Laboratory – sequence: 4 givenname: Guanjun surname: Jiang fullname: Jiang, Guanjun email: whq@buaa.edu.cn organization: School of Software, Beihang University,Beijing,China,100191 – sequence: 5 givenname: Haiquan surname: Wang fullname: Wang, Haiquan email: 1824055481@buaa.edu.cn organization: School of Software, Beihang University,Beijing,China,100191 – sequence: 6 givenname: Bowen surname: Du fullname: Du, Bowen email: dubowen@buaa.edu.cn organization: Zhongguancun Laboratory |
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| SubjectTerms | Accuracy Adaptation models Federated learning Satellites Schedules Simulation Training |
| Title | Hermes: Fast Semi-Asynchronous Federated Learning in LEO Constellations |
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