1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CR2024★ 1 cited
Dealing Doubt: Unveiling Threat Models in Gradient Inversion Attacks under Federated Learning, A Survey and Taxonomy
Yichuan Shi, Olivera Kotevska, Viktor Reshniak +2
Federated Learning (FL) has emerged as a leading paradigm for decentralized, privacy preserving machine learning training. However, recent research on gradient inversion attacks (G…
cs.LG2024★ 1 cited
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models
Abhishek Singh, Gauri Gupta, Ritvik Kapila +5
Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters. Our approach extends this concept b…