Federated learning (FL) enables multiple clients to collaboratively train machine learning
models without revealing their private training data. In conventional FL, the system follows …

In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the
edge receives a local model and updates the global model, effectively reducing the global …

Federated recommendation is a prominent use case within federated learning, yet it remains
susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attacks are …

Federated learning not only realizes collaborative training of models, but also effectively
maintains user privacy. However, with the widespread application of privacy-preserving …

Recent studies have revealed that federated learning (FL), once considered secure due to
clients not sharing their private data with the server, is vulnerable to attacks such as client …

Federated learning (FL) enables a set of geographically distributed clients to collectively
train a model through a server. Classically, the training process is synchronous, but can be …

Model poisoning attacks are critical security threats to Federated Learning (FL). Existing
model poisoning attacks suffer from two key limitations: 1) they achieve suboptimal …

Federated Learning (FL) offers a distributed framework to train a global control model across
multiple base stations without compromising the privacy of their local network data. This …

Byzantine-robust federated learning empowers the central server to acquire high-end global
models amidst a restrictive set of malicious clients. The general idea of existing learning …

Training a deep learning model from scratch requires a great deal of available labeled data,
computation resources, and expert knowledge. Thus, the time-consuming and complicated …