Federated learning (FL) has emerged as a key approach for training machine learning models without sharing raw data, making it highly relevant for privacy-sensitive applications. However, many existing FL frameworks rely on a central coordinator, which can introduce bottlenecks and… Read More »DEMO: NEBULA – Decentralized Federated Learning for Heterogeneous Networks
This paper introduces S-VOTE, a similarity-based voting mechanism designed to improve both efficiency and model performance in Decentralized Federated Learning (DFL). Unlike traditional federated learning, DFL operates without a central server, relying on peer-to-peer communication. While this avoids bottlenecks and… Read More »S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
This paper introduces ProFe, a new algorithm designed to make Decentralized Federated Learning (DFL) more communication-efficient without compromising model performance. In DFL, clients collaborate without a central server, which avoids single-point failures but creates significant communication overhead—especially when nodes have… Read More »ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes
As global connectivity ushers in the next era of communication, 6G networks are poised to revolutionize technology with unprecedented capabilities. These advancements will rely heavily on Artificial Intelligence (AI) and Machine Learning (ML), which are expected to enhance network efficiency,… Read More »Strengthening 6G AI/ML Security: Insights from ROBUST-6G Threat Assessment and Prevention Report
As the world transitions towards the sixth generation (6G) of wireless networks, ensuring robust security and trustworthiness solutions becomes a critical necessity. In this vein, the ROBUST-6G project aims to address key challenges related to security and trustworthiness through a… Read More »Exploring the Future of Secure 6G Networks: Insights from ROBUST-6G Deliverable 2.2