most citedEmpowering Data Mesh with Federated Learning

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CR2025

Practical Feasibility of Gradient Inversion Attacks in Federated Learning

Viktor Valadi, Mattias Åkesson, Lucas Beerens +4

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…

cs.LG2024★ 2 cited

Empowering Data Mesh with Federated Learning

Haoyuan Li, Salman Toor

The evolution of data architecture has seen the rise of data lakes, aiming to solve the bottlenecks of data management and promote intelligent decision-making. However, this centra…