{"title":"基于梯度稀疏的盲联合边缘学习最优MIMO组合","authors":"Ema Becirovic, Zheng Chen, E. Larsson","doi":"10.48550/arXiv.2203.12957","DOIUrl":null,"url":null,"abstract":"We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.","PeriodicalId":423807,"journal":{"name":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification\",\"authors\":\"Ema Becirovic, Zheng Chen, E. Larsson\",\"doi\":\"10.48550/arXiv.2203.12957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.\",\"PeriodicalId\":423807,\"journal\":{\"name\":\"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48550/arXiv.2203.12957\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2203.12957","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal MIMO Combining for Blind Federated Edge Learning with Gradient Sparsification
We provide the optimal receive combining strategy for federated learning in multiple-input multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform individual gradient sparsification which greatly improves performance in scenarios with heterogeneous (non i.i.d.) training data. The proposed method beats the benchmark by a wide margin.
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