Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated learning presents massive potential for privacy-friendly collaboration. However,
the performance of federated learning is deeply affected by byzantine attacks, where …

Federated learning presents massive potential for privacy-friendly vision task collaboration.
However, the federated visual performance is deeply affected by backdoor attacks, where …

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …

As a novel distributed learning paradigm, Federated Learning (FL) allows clients to train
global models collaboratively without exchanging private data. However, recent research …

Federated learning (FL) has shown great potential in medical image computing since it
provides a decentralized learning paradigm that allows multiple clients to train a model …

Federated Learning (FL) has emerged as a prominent solution for distributed learning
environments, enabling collaborative model training without centralized data collection …

Federated recommender systems (FRSs) utilize decentralized data to offer personalized and
privacy-preserving recommendations. Existing studies on FRSs overlook the issue of data …