In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Recent advances in machine learning have highlighted Federated Learning (FL) as a
promising approach that enables multiple distributed users (so-called clients) to collectively …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

Abstract The integration of Foundation Models (FMs) with Federated Learning (FL) presents
a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …

Although large-scale pre-trained models hold great potential for adapting to downstream
tasks through fine-tuning, the performance of such fine-tuned models is often limited by the …

Federated learning (FL) enables collaborative training of machine learning models across
distributed medical data sources without compromising privacy. However, applying FL to …

Federated learning (FL) is a distributed learning framework where users train a global model
by exchanging local model updates with a server instead of raw datasets, preserving data …

In recent years, cross-device federated learning (FL), particularly in the context of Internet of
Things (IoT) applications, has demonstrated its remarkable potential. Despite significant …

Most existing fingerprint recognition methods are based on machine learning and often
overlook the privacy and heterogeneity of data when training on large datasets, leading to …

Federated learning (FL) is a widely used distributed learning framework. However,
constrained wireless environment and intrinsically heterogeneous data across devices can …