5 papers · 1 filter
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…
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…
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…