581 citations · 1k across the 25 of their papers we have counts for
25 papers
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 Martínez 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…
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
Chao Feng, Nicolas Huber, Alberto Huertas Celdran +2
Federated Learning (FL) enables collaborative model training without sharing raw data, preserving participant privacy. Decentralized FL (DFL) eliminates reliance on a central serve…
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…
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…
Leveraging MTD to Mitigate Poisoning Attacks in Decentralized FL with Non-IID Data
Chao Feng, Alberto Huertas Celdrán, Zien Zeng +4
Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with…