14 citations · 32 across the 12 of their papers we have counts for
7 papers · 1 filter
Differentially private federated learning for localized control of infectious disease dynamics
Raouf Kerkouche, Henrik Zunker, Mario Fritz +1
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and…
DocMIA: Document-Level Membership Inference Attacks against DocVQA Models
Khanh Nguyen, Raouf Kerkouche, Mario Fritz +1
Document Visual Question Answering (DocVQA) has introduced a new paradigm for end-to-end document understanding, and quickly became one of the standard benchmarks for multimodal LL…
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators
Tejumade Afonja, Hui-Po Wang, Raouf Kerkouche +1
Generating tabular data under differential privacy (DP) protection ensures theoretical privacy guarantees but poses challenges for training machine learning models, primarily due t…
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
Hui-Po Wang, Dingfan Chen, Raouf Kerkouche +1
Conventional gradient-sharing approaches for federated learning (FL), such as FedAvg, rely on aggregation of local models and often face performance degradation under differential…
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data
Shadi Rahimian, Raouf Kerkouche, Ina Kurth +1
Survival analysis or time-to-event analysis aims to model and predict the time it takes for an event of interest to happen in a population or an individual. In the medical context…
Compression Boosts Differentially Private Federated Learning
Raouf Kerkouche, Gergely Ács, Claude Castelluccia +1
Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by excha…