2 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
When Topology Betrays Privacy: Lattice-Based Reconstruction Attacks on Secure Aggregation in Decentralized Federated Learning
Wenrui Yu, Changlong Ji, Johannes Bjerva +1
Secure Aggregation (SA) is widely regarded as a strong defense against model-update leakage in Federated Learning (FL), as it reveals only aggregate results while hiding individual…
A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG
Junjie Mu, Qiongxiu Li
Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because full local corpora remain on clients. As a result, routing must rely on c…
Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical Study
Changlong Ji, Stephane Maag, Richard Heusdens +1
Decentralized Federated Learning (DFL) has garnered attention for its robustness and scalability compared to Centralized Federated Learning (CFL). While DFL is commonly believed to…
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem
Qiongxiu Li, Lixia Luo, Agnese Gini +6
Federated Learning (FL) has emerged as a popular paradigm for collaborative learning among multiple parties. It is considered privacy-friendly because local data remains on persona…