Federated Learning (FL) is a distributed machine learning (ML) paradigm, aiming to train a
global model by exploiting the decentralized data across millions of edge devices …

Federated Learning (FL) typically involves a large-scale, distributed system with individual
user devices/servers training models locally and then aggregating their model updates on a …

Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data
privacy, existing passive mental health monitoring systems use alternative features such as …

Distributed machine learning approaches, including a broad class of federated learning
techniques, present a number of benefits when deploying machine learning applications …

Distributed machine learning approaches, including a broad class of federated learning (FL)
techniques, present a number of benefits when deploying machine learning applications …

Recently, Blockchain-Enabled Federated Learning (BCFL), an innovative approach that
combines the advantages of Federated Learning and Blockchain technology, is receiving …

Auction-based Federated Learning (AFL) has attracted extensive research interest due to its
ability to motivate data owners (DOs) to join FL through economic means. While many …

Federated learning (FL) enables edge devices to cooperatively train models without
exposing their raw data. However, implementing a practical FL system at the network edge …

Federated learning (FL) as an emerging distributed machine learning (ML) paradigm
enables participants to train their on-device data locally and share model parameters with …

Federated semi-supervised learning (FSSL) has been proposed to address the insufficient
labeled data problem by training models with pseudo-labeling. In previous FSSL systems, a …