15 papers
UnlinkableDFL: A Framework for Network-Layer Unlinkability in Decentralized Federated Learning
Chao Feng, Thomas Grubl, Jan von der Assen +4
Decentralized Federated Learning (DFL) removes the central aggregator of conventional Federated Learning, but peer-to-peer model exchange still exposes network traces: who communic…
A Crowdsensing Intrusion Detection Dataset For Decentralized Federated Learning Models
Chao Feng, Alberto Huertas Celdran, Jing Han +6
This paper introduces a dataset and an experimental study on Decentralized Federated Learning (DFL) for Internet of Things (IoT) crowdsensing malware detection. The dataset compris…
Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive HIL Testing
Chao Feng, Zihan Liu, Siddhant Gupta +3
Hardware-in-the-Loop (HIL) testing is essential for automotive validation but suffers from fragmented and underutilized test artifacts. This paper presents HIL-GPT, a retrieval-aug…
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…
Bridging Technical Capability and User Accessibility: Off-grid Civilian Emergency Communication
Karim Khamaisi, Oliver Kamer, Bruno Rodrigues +2
During large-scale crises disrupting cellular and Internet infrastructure, civilians lack reliable methods for communication, aid coordination, and access to trustworthy informatio…
GreenDFL: a Framework for Assessing the Sustainability of Decentralized Federated Learning Systems
Chao Feng, Alberto Huertas Celdrán, Xi Cheng +2
Decentralized Federated Learning (DFL) is an emerging paradigm that enables collaborative model training without centralized data and model aggregation, enhancing privacy and resil…