4 citations · 4 across the 3 of their papers we have counts for
4 papers · 1 filter
Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups
Aidmar Wainakh, Ephraim Zimmer, Sandeep Subedi +5
Federated learning (FL) enables a set of entities to collaboratively train a machine learning model without sharing their sensitive data, thus, mitigating some privacy concerns. Ho…
User-Level Label Leakage from Gradients in Federated Learning
Aidmar Wainakh, Fabrizio Ventola, Till Müßig +6
Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the…
Enhancing Privacy via Hierarchical Federated Learning
Aidmar Wainakh, Alejandro Sanchez Guinea, Tim Grube +1
Federated learning suffers from several privacy-related issues that expose the participants to various threats. A number of these issues are aggravated by the centralized architect…
On generating network traffic datasets with synthetic attacks for intrusion detection
Carlos Garcia Cordero, Emmanouil Vasilomanolakis, Aidmar Wainakh +2
Most research in the area of intrusion detection requires datasets to develop, evaluate or compare systems in one way or another. In this field, however, finding suitable datasets…