Re-architecting Personalized Federated Learning for Demanding Edge Environments
Rattachement africain : cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication. While knowledge cache-driven federated learning offers a promising FEL solution for demanding edge environments, its logits-based interaction design provides poor richness of exchanged information for on-device model optimization. To tackle this issue, we introduce DistilCacheFL, a novel personalized FEL architecture that enhances the exchange of optimization insights while delivering state-of-the-art performance with efficient communication. DistilCacheFL incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache, allowing devices to periodically download and utilize personalized knowledge for local model optimization. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) DistilCacheFL significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) DistilCacheFL can train splendid personalized on-device models with at least 28.6 improvement in communication efficiency.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Re-architecting Personalized Federated Learning for Demanding Edge Environments
- Date Crossref
- 14/03/2026
- Éditeur
- Association for the Advancement of Artificial Intelligence (AAAI)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Institute of Computing Technology pays non établi dans la noticeStructure de recherche
-
Chinese Academy of Sciences Institute of Computing Technology pays non établi dans la noticeOrganisme public
-
China Mobile (China) pays non établi dans la noticeEntreprise
-
China Mobile Research Institute pays non établi dans la noticeEntreprise
-
Beijing Jiaotong University pays non établi dans la noticeUniversité ou école supérieure
-
Beihang University MOE Engineering Research Center of Advanced Computer Application Technology pays non établi dans la noticeUniversité ou école supérieure
-
School of Computer and Information Technology pays non établi dans la noticeUniversité ou école supérieure
Institute of Computing Technology, Institute of Computing Technology — Chinese Academy of Sciences et China Mobile (China), avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.