Jan 16, 2024 · Abstract:Personalized federated learning aims to address data heterogeneity across local clients in federated learning.
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FL also has a personalization branch, which aims to customize local models to improve their performance on local data. AmazonScience_FederatedLearning.gif.
Dec 2, 2019 · This paper pro-posesFedPer, a base + personalization layer approach for federated training of deep feedforward neural networks, which can combat ...
May 12, 2023 · Model personalization is a common technique used to improve model performance in FL when data heterogeneity (i.e. non-identically distributed ...
Personalized Federated Learning (pFL) holds immense promise for tailoring machine learning models to individual users while preserving data privacy.
Jun 1, 2024 · This technique involves generating new training data by applying transformations to existing data. This can be used for personalization by ...
In this paper, we study a personalized variant of the federated learning in which our goal is to find an initial shared model that current or new users can ...
Feb 13, 2024 · Federated learning enables multiple clients to collaboratively learn machine learning models in a privacy-preserving manner.
Personalized federated learning aims to address data heterogeneity across local clients in federated learning. However, current methods blindly incorporate ...
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The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global ...
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