4 citations · 4 across the 4 of their papers we have counts for
6 papers
Coded Task Offloading for Fluid Computing: A Privacy-Aware Approach under D2D Networks
Diego Cajaraville-Aboy, Manuel Fernández-Veiga, Ana Fernández-Vilas +1
Fluid Computing aims to support distributed applications execution across heterogeneous cloud, edge, and device resources, motivating task execution mechanisms that adapt to dynami…
Air-Plan: Query-Optimized Topology Selection for Over-the-Air Decentralized Federated Learning
Kaushal Attaluri, Rebeca P. Diaz-Redondo, Manuel Fernandez Veiga
Over-the-air (OTA) aggregation exploits the superposition property of wireless multiple-access channels to aggregate model updates from multiple devices within a single transmissio…
Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
Xavier MartÃnez-Luaña, Alba Gude-Santos, Manuel Fernández-Veiga +1
Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy leakage and malicious manipulation. E…
Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. DÃaz-Redondo +1
Federated Learning (FL) emerges as a distributed machine learning approach that addresses privacy concerns by training AI models locally on devices. Decentralized Federated Learnin…
A Blockchain Solution for Collaborative Machine Learning over IoT
Carlos Beis-Penedo, Francisco Troncoso-Pastoriza, Rebeca P. DÃaz-Redondo +3
The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the…
Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulator
Alberto Bazán-Guillén, Carlos Beis-Penedo, Diego Cajaraville-Aboy +6
Realistic urban traffic simulation is essential for sustainable urban planning and the development of intelligent transportation systems. However, generating high-fidelity, time-va…