Sodium-based rechargeable batteries are very promising energy storage and conversion
systems owing to their wide availability and the low cost of Na resources, which is beneficial …

The federated learning (FL) framework enables edge clients to collaboratively learn a
shared inference model while keeping privacy of training data on clients. Recently, many …

Federated learning (FL) is a promising paradigm to enable collaborative model training with
decentralized data. However, the training process of Large Language Models (LLMs) …

As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …

Digital marketplaces were created recently to accelerate the delivery of applications and
services to customers. Their appealing feature is to activate and dynamize the demand …

Recent studies on federated learning (FL) have sought to solve the system heterogeneity
issue by designing customized local models for different clients. However, public dataset …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …

Abstract The advent of Big Data era drives data analysts from different domains to use data
mining techniques for data analysis. However, performing data analysis in a specific domain …

The emergence of technologies like Big data analytics, Industrial Internet of Things, Internet
of Things, and applicability of these technologies in various domains leads to increased …

Graph matching in the setting of federated learning is still an open problem. This paper
proposes an unsupervised federated graph matching algorithm, UFGM, for inferring …