8 papers
ColNet: Collaborative Optimization in Decentralized Federated Multi-task Learning Systems
Chao Feng, Nicolas Fazli Kohler, Zhi Wang +4
The integration of Federated Learning (FL) and Multi-Task Learning (MTL) has been explored to address client heterogeneity, with Federated Multi-Task Learning (FMTL) treating each…
TemporalFED: Detecting Cyberattacks in Industrial Time-Series Data Using Decentralized Federated Learning
Ãngel Luis Perales Gómez, Enrique Tomás MartÃnez Beltrán, Pedro Miguel Sánchez Sánchez +1
Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues such as cyberattacks affectin…
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…
From Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning
Chao Feng, Yuanzhe Gao, Alberto Huertas Celdran +2
Federated Learning (FL) is widely recognized as a privacy-preserving Machine Learning paradigm due to its model-sharing mechanism that avoids direct data exchange. Nevertheless, mo…
FEST: A Unified Framework for Evaluating Synthetic Tabular Data
Weijie Niu, Alberto Huertas Celdran, Karoline Siarsky +1
Synthetic data generation, leveraging generative machine learning techniques, offers a promising approach to mitigating privacy concerns associated with real-world data usage. Synt…
RepuNet: A Reputation System for Mitigating Malicious Clients in DFL
Isaac Marroqui Penalva, Enrique Tomás MartÃnez Beltrán, Manuel Gil Pérez +1
Decentralized Federated Learning (DFL) enables nodes to collaboratively train models without a central server, introducing new vulnerabilities since each node independently selects…