The 33th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning will be organized in hybrid mode, from Wednesday 23 to Friday 25 April 2025. Both in-person and online participation will be possible.
On Friday, April 25, at 10:45 AM, UMons will present a paper written in collaboration with CETIC.
Title - SecureBFL: a Blockchain-enhanced federated learning architecture with MPC
Abstract : The increasing demand for data in machine learning raises significant privacy concerns. Federated Learning (FL) enables multiple entities to train models collaboratively without sharing raw data. However, centralized FL (CFL) relies on a central server, making it vulnerable to poisoning attacks and single points of failure (SPOF). Decentralized FL (DFL) addresses these issues by removing the central server. This paper proposes a novel DFL architecture integrating blockchain for resisting attacks and Multi-Party Computation (MPC) for secure model parameter transfer. This architecture enhances security and confidentiality in collaborative learning without compromising result quality.
Authors: Tanguy Vansnick - University of Mons and Leandro Collier, Research Engineer at CETIC
ESAN 2025 Program
View online : https://www.esann.org/