82 citations · 111 across the 18 of their papers we have counts for
6 papers · 1 filter
Assessing the Sustainability and Trustworthiness of Federated Learning Models
Chao Feng, Alberto Huertas Celdran, Pedro Miguel Sanchez Sanchez +3
Artificial intelligence (AI) increasingly influences critical decision-making across sectors. Federated Learning (FL), as a privacy-preserving collaborative AI paradigm, not only e…
Voyager: MTD-Based Aggregation Protocol for Mitigating Poisoning Attacks on DFL
Chao Feng, Alberto Huertas Celdran, Michael Vuong +2
The growing concern over malicious attacks targeting the robustness of both Centralized and Decentralized Federated Learning (FL) necessitates novel defensive strategies. In contra…
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning
Chao Feng, Alberto Huertas Celdrán, Janosch Baltensperger +4
Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and…
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation
Chao Feng, Alberto Huertas Celdran, Pedro Miguel Sanchez Sanchez +5
Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevert…
RCVaR: an Economic Approach to Estimate Cyberattacks Costs using Data from Industry Reports
Muriel Figueredo Franco, Fabian Künzler, Jan von der Assen +2
Digitization increases business opportunities and the risk of companies being victims of devastating cyberattacks. Therefore, managing risk exposure and cybersecurity strategies is…
Fedstellar: A Platform for Decentralized Federated Learning
Enrique Tomás Martínez Beltrán, Ángel Luis Perales Gómez, Chao Feng +6
In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy. Si…