2 papers
cs.CR2021
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…
cs.CR2021
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…