Titre :

Verifiable cross-silo federated learning

Auteur(s) :

Korneev, Aleksei [Auteur]
Machine Learning in Information Networks [MAGNET]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
Ramon, Jan [Auteur]
Machine Learning in Information Networks [MAGNET]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille

Date de publication :

2024

Mot(s)-clé(s) en anglais :

federated learning
FL
cross-silo FL
verification
verifiable protocols
zero knowledge proofs
federated learning FL cross-silo FL verification verifiable protocols zero knowledge proofs

Discipline(s) HAL :

Informatique [cs]

Résumé en anglais : [en]

Federated Learning (FL) is a widespread approach that allows training machine learning (ML) models with data distributed across multiple devices. In cross-silo FL, which often appears in domains like healthcare or finance, ...
Lire la suite >Federated Learning (FL) is a widespread approach that allows training machine learning (ML) models with data distributed across multiple devices. In cross-silo FL, which often appears in domains like healthcare or finance, the number of participants is moderate, and each party typically represents a well-known organization. However, malicious agents may still attempt to disturb the training procedure in order to obtain certain benefits, for example, a biased result or a reduction in computational load. While one can easily detect a malicious agent when data used for training is public, the problem becomes much more acute when it is necessary to maintain the privacy of the training dataset. To address this issue, there is recently growing interest in developing verifiable protocols, where one can check that parties do not deviate from the training procedure and perform computations correctly. In this paper, we conduct a comprehensive analysis of such protocols, and fit them in a taxonomy. We perform a comparison of the efficiency and threat models of various approaches. We next identify research gaps and discuss potential directions for future scientific work.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL