June 11, 2024, 4:47 a.m. | Basem Suleiman, Muhammad Johan Alibasa, Rizka Widyarini Purwanto, Lewis Jeffries, Ali Anaissi, Jacky Song

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.06340v1 Announce Type: new
Abstract: Federated Learning (FL) enables local devices to collaboratively learn a shared predictive model by only periodically sharing model parameters with a central aggregator. However, FL can be disadvantaged by statistical heterogeneity produced by the diversity in each local devices data distribution, which creates different levels of Independent and Identically Distributed (IID) data. Furthermore, this can be more complex when optimising different combinations of FL parameters and choosing optimal aggregation. In this paper, we present an …

abstract arxiv cs.ai cs.lg data devices distribution diversity federated learning however learn optimisation parameters predictive statistical type

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