A Review of Split Learning and Federated Learning: Challenges and Synergies

Résumé

Split Learning and Federated Learning have emerged as key techniques in the domain of privacy-preserving distributed machine learning. This paper reviews the recent developments in both paradigms, discussing their respective advantages, limitations, and the potential for their integration. We provide an analysis of current research trends, explore challenges in implementation, and suggest future directions for improving these approaches. The review serves as a resource for researchers and practitioners interested in the evolving landscape of distributed machine learning.

Citer ce document

Accès au document

Texte intégral en lecture en ligne, réservé aux abonnés SPHAERO et aux membres de l'institution. Se connecter

Voir l'article sur le site de la revue

Auteur(s)

Statistiques

Consultations : 1

Téléchargements : 0