The distributed nature of Federated Learning (FL) introduces security vulnerabilities and
issues related to the heterogeneous distribution of data. Traditional FL aggregation …

As edge computing complements the cloud to enable computational services right at the
network edge, federated learning (FL) can also benefit from close-by edge computing …

Federated learning (FL) is a viable technique to train a shared machine learning model
without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its …

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

With the rapid development of big data, Federated learning (FL) has found numerous
applications, enabling machine learning (ML) on edge devices while preserving privacy …

The reliance of language model training on massive amounts of computation and vast
datasets scraped from potentially low-quality, copyrighted, or sensitive data has come into …

Federated learning (FL) represents a decentralized machine learning paradigm where
multiple clients cooperatively train a global model over local data, under the coordination of …

The emerging Federated Learning (FL) permits all workers (eg, mobile devices) to
cooperatively train a model using their local data at the network edge. In order to avoid the …

Hierarchical clustering learning (HCL) extends traditional parameter server-based
distributed learning by clustering heterogeneous user equipments (UEs) via cluster nodes …

To overcome the limitations of long latency and privacy concerns from cloud computing,
edge computing along with distributed machine learning such as federated learning (FL) …