Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

With the increasing number of connected vehicles on the road, vehicular communications
have become an important research area. Federated learning (FL), a distributed machine …

Advancements in Vehicular ad-hoc Network (VANET) technology have led to a growing
network of interconnected devices, including edge devices, resulting in substantial data …

Meta-analysis has been established as an effective approach to combining summary
statistics of several genome-wide association studies (GWAS). However, the accuracy of …

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of
differential expression analyses, yielding deeper clinical insights. As data exchange is often …

Federated learning (FL) has emerged as a privacy-aware alternative to centralized data
analysis, especially for biomedical analyses such as genome-wide association studies …

Federated association testing is a powerful approach to conduct large-scale association
studies where sites share intermediate statistics through a central server. There are …

The federated learning on large-scale mobile terminals and Internet of Things (IoT) devices
faces the issues of privacy leakage, resource limitation, and frequent user dropouts. This …

Federated Learning (FL) is a new paradigm aimed at solving data access problems. It
provides a solution by moving the focus from sharing data to sharing models. The FL …

We present a novel strategy for detecting global outliers in a federated learning setting,
targeting in particular cross-silo scenarios. Our approach involves the use of two servers and …