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Computer Science > Machine Learning

arXiv:2306.05515 (cs)
[Submitted on 8 Jun 2023 (v1), last revised 16 Jan 2025 (this version, v4)]

Title:PeFLL: Personalized Federated Learning by Learning to Learn

Authors:Jonathan Scott, Hossein Zakerinia, Christoph H. Lampert
View a PDF of the paper titled PeFLL: Personalized Federated Learning by Learning to Learn, by Jonathan Scott and 2 other authors
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Abstract:We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation and client-server communication by providing future clients with ready-to-use personalized models that require no additional finetuning or optimization; 3) it comes with theoretical guarantees that establish generalization from the observed clients to future ones. At the core of PeFLL lies a learning-to-learn approach that jointly trains an embedding network and a hypernetwork. The embedding network is used to represent clients in a latent descriptor space in a way that reflects their similarity to each other. The hypernetwork takes as input such descriptors and outputs the parameters of fully personalized client models. In combination, both networks constitute a learning algorithm that achieves state-of-the-art performance in several personalized federated learning benchmarks.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2306.05515 [cs.LG]
  (or arXiv:2306.05515v4 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2306.05515
arXiv-issued DOI via DataCite

Submission history

From: Jonathan Scott [view email]
[v1] Thu, 8 Jun 2023 19:12:42 UTC (84 KB)
[v2] Wed, 25 Oct 2023 15:15:28 UTC (123 KB)
[v3] Mon, 13 May 2024 14:19:58 UTC (418 KB)
[v4] Thu, 16 Jan 2025 08:53:23 UTC (425 KB)
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