Federated learning (FL) is a new machine learning paradigm to overcome the challenge of
data silos and has garnered significant attention. However, through our observations, a …

Federated learning (FL) has garnered considerable attention due to its privacy-preserving
feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness …

Federated Learning (FL) is a decentralized machine learning (ML) approach that keeps data
localized and often incorporates Differential Privacy (DP) to enhance privacy guarantees …

Federated learning (FL) aims to collaboratively train the global model in a distributed
manner by sharing the model parameters from local clients to a central server, thereby …

As a means of decentralized machine learning, federated learning (FL) has recently drawn
considerable attentions. One of the prominent advantages of FL is its capability of preventing …

Federated learning (FL) empowers distributed clients to collaboratively train a shared
machine learning model through exchanging parameter information. Despite the fact that FL …

A key feature of federated learning (FL) is to preserve the data privacy of end users.
However, there still exist potential privacy leakage in exchanging gradients under FL. As a …

Federated Learning (FL) has increasingly been recognized as an innovative and secure
distributed model training paradigm, aiming to coordinate multiple edge clients to …

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …

Federated learning (FL) is a privacy-preserving learning technique that enables distributed
computing devices to train shared learning models across data silos collaboratively. Existing …