Federated learning has been rapidly evolving and gaining popularity in recent years due to
its privacy-preserving features, among other advantages. Nevertheless, the exchange of …

Federated learning can effectively ensure data security and improve the problem of data
islanding. However, the performance of federated learning-based schemes could be better …

Federated Learning is highly susceptible to backdoor and targeted attacks as participants
can manipulate their data and models locally without any oversight on whether they follow …

Federated learning enables cooperative computation between multiple participants while
protecting user privacy. Currently, federated learning algorithms assume that all participants …

The Smart Grid (SG) is a critical energy infrastructure that collects real-time electricity usage
data to forecast future energy demands using information and communication technologies …

Federated learning (FL) is widely used in edge environments as a privacy-preserving
collaborative learning paradigm. However, edge devices often have heterogeneous …

Federated recommendation systems employ federated learning techniques to safeguard
user privacy by transmitting model parameters instead of raw user data between user …

As an emerging decentralized machine learning technique, federated learning organizes
collaborative training and preserves the privacy and security of participants. However …

This paper studies distributed online learning under Byzantine attacks. The performance of
an online learning algorithm is often characterized by (adversarial) regret, which evaluates …