Personalized federated learning aims to address data heterogeneity across local clients in
federated learning. However, current methods blindly incorporate either full model …

The prevalent personalized federated learning (PFL) usually pursues a trade-off between
personalization and generalization by maintaining a shared global model to guide the …

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 …

Personalized federated learning becomes a hot research topic that can learn a personalized
learning model for each client. Existing personalized federated learning models prefer to …

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 real-world federated learning scenarios, participants could have their own personalized
labels which are incompatible with those from other clients, due to using different label …

In real-world federated learning scenarios, participants could have their own personalized
labels incompatible with those from other clients, due to using different label permutations or …

Personalized federated learning (FL) facilitates collaborations between multiple clients to
learn personalized models without sharing private data. The mechanism mitigates the …

While federated learning traditionally aims to train a single global model across
decentralized local datasets, one model may not always be ideal for all participating clients …

Personalized Federated Learning (PFL) relies on collective data knowledge to build
customized models. However, non-IID data between clients poses significant challenges, as …