82 citations · 111 across the 18 of their papers we have counts for
7 papers · 1 filter
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…
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation
Heqing Ren, Chao Feng, Alberto Huertas +1
Traditional machine learning (ML) raises serious privacy concerns, while federated learning (FL) mitigates the risk of data leakage by keeping data on local devices. However, the t…
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…
FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning
Chao Feng, Hongjie Guan, Alberto Huertas Celdrán +3
Non-Independent and Identically Distributed (non-IID) data in Federated Learning (FL) causes client drift issues, leading to slower convergence and reduced model performance. While…
De-VertiFL: A Solution for Decentralized Vertical Federated Learning
Alberto Huertas Celdrán, Chao Feng, Sabyasachi Banik +3
Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients…
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…