Inferring Contributions in Privacy-Preserving Federated Learning
Other text in scientific journal, 2025

To what extent do individual contributions enhance the overall outcome of collaborative work? This question naturally arises across scientific fields and is particularly challenging in Federated Learning. It remains largely unexplored in privacy-preserving settings where individual actions are concealed with techniques like Secure Aggregation.

Author

Balazs Pejo

Budapest University of Technology and Economics

Delio Jaramillo Velez

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

ERCIM NEWS

0926-4981 (ISSN) 1564-0094 (eISSN)

Vol. In Press 140

Subject Categories (SSIF 2025)

Computer Sciences

More information

Latest update

5/2/2025 1