1 code implementation • 8 Jun 2023 • Jonathan Scott, Hossein Zakerinia, Christoph H. Lampert
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.
1 code implementation • 12 Oct 2022 • Jonathan Scott, Michelle Yeo, Christoph H. Lampert
We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning.
no code implementations • 8 Oct 2021 • Marcus T. Wilson, Vance Farrow, Caleb Pyne, Jonathan Scott
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