Computer Science > Machine Learning
arXiv:2308.13265 (cs)
[Submitted on 25 Aug 2023 (v1), last revised 25 Jan 2024 (this version, v2)]
Title:Heterogeneous Federated Learning via Personalized Generative Networks
View a PDF of the paper titled Heterogeneous Federated Learning via Personalized Generative Networks, by Zahra Taghiyarrenani and 3 other authors
View PDFAbstract:Federated Learning (FL) allows several clients to construct a common global machine-learning model without having to share their data. FL, however, faces the challenge of statistical heterogeneity between the client's data, which degrades performance and slows down the convergence toward the global model. In this paper, we provide theoretical proof that minimizing heterogeneity between clients facilitates the convergence of a global model for every single client. This becomes particularly important under empirical concept shifts among clients, rather than merely considering imbalanced classes, which have been studied until now. Therefore, we propose a method for knowledge transfer between clients where the server trains client-specific generators. Each generator generates samples for the corresponding client to remove the conflict with other clients' models. Experiments conducted on synthetic and real data, along with a theoretical study, support the effectiveness of our method in constructing a well-generalizable global model by reducing the conflict between local models.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2308.13265 [cs.LG] |
| (orarXiv:2308.13265v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2308.13265 arXiv-issued DOI via DataCite |
Submission history
From: Zahra Taghiyarrenani Ms [view email][v1] Fri, 25 Aug 2023 09:37:02 UTC (3,168 KB)
[v2] Thu, 25 Jan 2024 10:16:46 UTC (3,198 KB)
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View a PDF of the paper titled Heterogeneous Federated Learning via Personalized Generative Networks, by Zahra Taghiyarrenani and 3 other authors
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