Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL)
is to decouple general knowledge (shared among clients) and client-specific knowledge, as …

In personalized federated learning (PFL), it is widely recognized that achieving both high
model generalization and effective personalization poses a significant challenge due to their …

Privacy protection in recommendation systems is gaining increasing attention, for which
federated learning has emerged as a promising solution. Current federated …

Federated learning (FL) is a promising approach for learning a model from data distributed
on massive clients without exposing data privacy. It works effectively in the ideal federation …