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SecureBFL: a Blockchain-enhanced federated learning architecture with MPC

Year: 2025

Type: conference-paper

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.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-160

Authors: Tanguy Vansnick, Leandro Collier, Saïd Mahmoudi

Institutions: University of Mons, Centre d'Excellence en Technologies de l'Information et de la Communication

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409479806

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