Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Federated learning shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …

Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing
to the advantages of federated learning, federated graph learning (FGL) enables clients to …

With the flourish of digital technologies and rapid development of 5G and beyond networks,
Metaverse has become an increasingly hotly discussed topic, which offers users with …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Multicast short video streaming (MSVS) can effectively reduce network traffic load by
delivering identical video sequences to multiple users simultaneously. The existing MSVS …

This paper focuses on the problem of personalized federated learning (FL) with the schema
of contrastive learning (CL), which is to implement collaborative pattern classification by …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …