This paper examines how federated learning (FL) can be made more reliable and efficient in vehicular environments, where vehicles act as learning clients while continuously moving across the road network. Mobility creates challenges such as fluctuating wireless connectivity, variable computation… Read More »VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
Training spiking neural networks (SNNs) remains a longstanding challenge due to their non-differentiable spike activation functions. Most existing approaches adapt methods designed for traditional artificial neural networks, such as backpropagation with surrogate gradients. While widely used, these surrogate techniques introduce… Read More »ADMM-Based Training for Spiking Neural Networks
This paper introduces ROBUST-6G, a European research initiative aimed at designing a comprehensive security platform for future 6G networks. As 6G moves toward deeply integrated digital: physical systems, the project focuses on building security mechanisms that are data-driven, automated, trustworthy,… Read More »ROBUST-6G: Smart, Automated, and Reliable Security Service Platform for 6G
As large language models continue to grow, running them efficiently on small edge devices remains one of AI’s biggest challenges. This paper explores how LLM inference can be decentralized across a network of energy-harvesting devices each powered by intermittent renewable… Read More »Decentralized LLM Inference over Edge Networks with Energy Harvesting
As 6G emerges as a transformative force in digital communication, its ability to seamlessly integrate the digital and physical worlds hinges on robust security frameworks. ROBUST-6G’s Deliverable 2.1, “6G Threat Analysis Report,” lays the groundwork for addressing this pivotal challenge,… Read More »Unveiling the Future of 6G Security with ROBUST-6G’s Threat Analysis