{"title":"海报:用半可靠的空中多播模型加速跨设备联邦学习","authors":"Yunzhi Lin, Shouxi Luo","doi":"10.1109/ICNP52444.2021.9651964","DOIUrl":null,"url":null,"abstract":"To achieve efficient model multicast for cross-device Federated Learning (FL) over shared wireless channels, we propose SRMP, a transport protocol that performs semi-reliable model multicast over the air by leveraging existing PHY-aided wireless multicast techniques. The preliminary study shows that, with novel designs, SRMP could reduce the communication time involved in each round of training significantly.","PeriodicalId":343813,"journal":{"name":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Poster: Accelerate Cross-Device Federated Learning With Semi-Reliable Model Multicast Over The Air\",\"authors\":\"Yunzhi Lin, Shouxi Luo\",\"doi\":\"10.1109/ICNP52444.2021.9651964\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To achieve efficient model multicast for cross-device Federated Learning (FL) over shared wireless channels, we propose SRMP, a transport protocol that performs semi-reliable model multicast over the air by leveraging existing PHY-aided wireless multicast techniques. The preliminary study shows that, with novel designs, SRMP could reduce the communication time involved in each round of training significantly.\",\"PeriodicalId\":343813,\"journal\":{\"name\":\"2021 IEEE 29th International Conference on Network Protocols (ICNP)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 29th International Conference on Network Protocols (ICNP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNP52444.2021.9651964\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 29th International Conference on Network Protocols (ICNP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNP52444.2021.9651964","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poster: Accelerate Cross-Device Federated Learning With Semi-Reliable Model Multicast Over The Air
To achieve efficient model multicast for cross-device Federated Learning (FL) over shared wireless channels, we propose SRMP, a transport protocol that performs semi-reliable model multicast over the air by leveraging existing PHY-aided wireless multicast techniques. The preliminary study shows that, with novel designs, SRMP could reduce the communication time involved in each round of training significantly.
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