Federated learning (FL) provides a decentralized machine learning paradigm where a
server collaborates with a group of clients to learn a global model without accessing the …

Federated Learning (FL) has recently emerged as a novel machine learning paradigm
allowing to preserve privacy and to account for the distributed nature of the learning process …

Federated learning (FL) provides a decentralized machine learning paradigm where a
server collaborates with a group of clients to learn a global model without accessing the …

Abstract Personalized Federated Learning (PFL) is proposed to find the greatest
personalized models for each client. To avoid the central failure and communication …

In mobile and IoT systems, Federated Learning (FL) is increasingly important for effectively
using data while maintaining user privacy. One key challenge in FL is managing statistical …

Previous studies on federated learning (FL) often encounter performance degradation due
to data heterogeneity among different clients. In light of the recent advances in multimodal …

Federated learning is a distributed paradigm that allows multiple parties to collaboratively
train deep learning models without direct exchange of raw data. Nevertheless, the inherent …

Federated learning, as a privacy-preserving learning paradigm, restricts the access to data
of each local client, for protecting the privacy of the parties. However, in the case of …

Collaborative fairness stands as an essential element in federated learning to encourage
client participation by equitably distributing rewards based on individual contributions …

Federated learning is a distributed optimization paradigm that allows training machine
learning models across decentralized devices while keeping the data localized. The …