Resource-Efficient Personalization in Federated Learning With Closed-Form Classifiers

Resource-Efficient Personalization in Federated Learning With Closed-Form Classifiers

Statistical heterogeneity in Federated Learning (FL) often leads to client drift and biased local solutions. Prior work in the literature shows that client drift particularly affects the parameters of the classification layer, hindering both convergence and accuracy. While Personalized FL (PFL) addr...

Full description

Saved in:
Bibliographic Details
Main Authors: Eros Fani, Raffaello Camoriano, Barbara Caputo, Marco Ciccone
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Federated learning
personalized federated learning
pre-trained models
ridge regression
closed-form classifiers
Online Access:https://ieeexplore.ieee.org/document/10946159/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10946159/

Similar Items