Federated learning allows multiple clients to collaboratively train a model without
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …

While federated learning (FL) is promising for efficient collaborative learning without
revealing local data, it remains vulnerable to white-box privacy attacks, suffers from high …

Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural
networks on edge devices without collecting data at a central server. However, FL …

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 (FL) is a distributed learning paradigm that maximizes the potential of
data-driven models for edge devices without sharing their raw data. However, devices often …

Federated Learning (FL) has emerged as a solution for distributed systems that allow clients
to train models on their data and only share models instead of local data. Generative Models …

The increasing privacy concerns on personal private text data promote the development of
federated learning (FL) in recent years. However, the existing studies on applying FL in NLP …

Recently, semi-supervised federated learning (semi-FL) has been proposed to handle the
commonly seen real-world scenarios with labeled data on the server and unlabeled data on …

Model averaging is a widely adopted technique in federated learning (FL) that aggregates
multiple client models to obtain a global model. Remarkably, model averaging in FL yields a …

Federated learning (FL) enables multiple clients to train a model while keeping their data
private collaboratively. Previous studies have shown that data heterogeneity between clients …