2 papers
cs.CR2026
Practical Feasibility of Gradient Inversion Attacks in Federated Learning
Viktor Valadi, Mattias à kesson, Johan Ãstman +3
Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable exp…
cs.IR2025
: Hierarchical Information Extraction via Encoding and Embedding
Tianru Zhang, Li Ju, Prashant Singh +1
Analyzing large-scale datasets, especially involving complex and high-dimensional data like images, is particularly challenging. While self-supervised learning (SSL) has proven eff…