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 (FL) facilitates distributed training across different IoT and edge devices,
safeguarding the privacy of their data. The inherent distributed structure of FL introduces …

Federated learning (FL) has gained widespread popularity in a variety of industries due to its
ability to locally train models on devices while preserving privacy. However, FL systems are …

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 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 (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 …

Federated learning has gained popularity as it enables collaborative training without sharing
local data. Despite its advantages, federated learning requires sharing the model …

This paper introduces FedSecurity, an end-to-end benchmark that serves as a
supplementary component of the FedML library for simulating adversarial attacks and …