Copertina

Client-supervised Federated Learning: Towards One-model-for-all Personalization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yan, Peng, Long, Guodong
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Personalized One-shot Federated Graph Learning for Heterogeneous Clients
di: Yan, Guochen, et al.
Pubblicazione: (2024)

Personalized Interpretation on Federated Learning: A Virtual Concepts approach
di: Yan, Peng, et al.
Pubblicazione: (2024)

Federated Recommendation with Additive Personalization
di: Li, Zhiwei, et al.
Pubblicazione: (2023)

Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models
di: Ye, Wenxuan, et al.
Pubblicazione: (2025)

Influence-oriented Personalized Federated Learning
di: Tan, Yue, et al.
Pubblicazione: (2024)

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
di: Zakerinia, Hossein, et al.
Pubblicazione: (2025)

FedMerge: Federated Personalization via Model Merging
di: Chen, Shutong, et al.
Pubblicazione: (2025)

Towards Client Driven Federated Learning
di: Li, Songze, et al.
Pubblicazione: (2024)

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
di: Allouah, Youssef, et al.
Pubblicazione: (2024)

Personalized Federated Learning with Attention-based Client Selection
di: Chen, Zihan, et al.
Pubblicazione: (2023)

FedDAPL: Toward Client-Private Generalization in Federated Learning
di: Loaliyan, Soroosh Safari, et al.
Pubblicazione: (2025)

Federated Weather Modeling on Sensor Data
di: Chen, Shengchao, et al.
Pubblicazione: (2026)

Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
di: de Souza, Allan M., et al.
Pubblicazione: (2024)

Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach
di: Yang, Yiyuan, et al.
Pubblicazione: (2025)

FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning
di: Peng, Fu, et al.
Pubblicazione: (2025)

Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
di: Wang, Chuanyin, et al.
Pubblicazione: (2025)

Dual-Personalizing Adapter for Federated Foundation Models
di: Yang, Yiyuan, et al.
Pubblicazione: (2024)

Federated Vision-Language-Recommendation with Personalized Fusion
di: Li, Zhiwei, et al.
Pubblicazione: (2024)

PSI-PFL: Population Stability Index for Client Selection in non-IID Personalized Federated Learning
di: Jimenez-Gutierrez, Daniel-M., et al.
Pubblicazione: (2025)

Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization
di: Puppala, Sai, et al.
Pubblicazione: (2025)

An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning
di: Chen, Yan-Ann, et al.
Pubblicazione: (2025)

Toward Malicious Clients Detection in Federated Learning
di: Dou, Zhihao, et al.
Pubblicazione: (2025)

The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
di: Wu, Xinghao, et al.
Pubblicazione: (2024)

Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
di: Ma, Mengmeng, et al.
Pubblicazione: (2024)

Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning
di: Chen, Huancheng, et al.
Pubblicazione: (2023)

Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
di: Chen, Shengchao, et al.
Pubblicazione: (2026)

Client Contribution Normalization for Enhanced Federated Learning
di: Kundalwal, Mayank Kumar, et al.
Pubblicazione: (2024)

Rethinking Client-oriented Federated Graph Learning
di: Chen, Zekai, et al.
Pubblicazione: (2025)

Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
di: Chen, Shengchao, et al.
Pubblicazione: (2024)

A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendation
di: Li, Zhiwei, et al.
Pubblicazione: (2025)

Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing
di: Guo, Yongxin, et al.
Pubblicazione: (2024)

Federated Prompt Learning for Weather Foundation Models on Devices
di: Chen, Shengchao, et al.
Pubblicazione: (2023)

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
di: Li, Zhiwei, et al.
Pubblicazione: (2024)

FeDaL: Federated Dataset Learning for General Time Series Foundation Models
di: Chen, Shengchao, et al.
Pubblicazione: (2025)

The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics
di: Turazza, Fabio, et al.
Pubblicazione: (2026)

Distilling A Universal Expert from Clustered Federated Learning
di: Leng, Zeqi, et al.
Pubblicazione: (2025)

Distributionally Robust Federated Learning with Client Drift Minimization
di: Krouka, Mounssif, et al.
Pubblicazione: (2025)

LEFL: Low Entropy Client Sampling in Federated Learning
di: Abebe, Waqwoya, et al.
Pubblicazione: (2023)

Communication-Efficient Federated Learning With Data and Client Heterogeneity
di: Zakerinia, Hossein, et al.
Pubblicazione: (2022)

Optimal Strategies for Federated Learning Maintaining Client Privacy
di: Bhaskar, Uday, et al.
Pubblicazione: (2025)