Copertina

Influence-oriented Personalized Federated Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Tan, Yue, Long, Guodong, Jiang, Jing, Zhang, Chengqi
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

FedAH: Aggregated Head for Personalized Federated Learning
di: Zhou, Pengzhan, et al.
Pubblicazione: (2024)

Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning
di: Chen, Sijia, et al.
Pubblicazione: (2024)

Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity
di: Wang, Zheng, et al.
Pubblicazione: (2024)

Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
di: Lee, Seunghyun, et al.
Pubblicazione: (2025)

A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
di: Chen, Minghui, et al.
Pubblicazione: (2025)

AugFL: Augmenting Federated Learning with Pretrained Models
di: Yue, Sheng, et al.
Pubblicazione: (2025)

FedMT: Federated Learning with Mixed-type Labels
di: Zhang, Qiong, et al.
Pubblicazione: (2022)

Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
di: Liu, Ruyue, et al.
Pubblicazione: (2024)

DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models
di: Chang, Ying, et al.
Pubblicazione: (2025)

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
di: Ji, Shaoxiong, et al.
Pubblicazione: (2021)

Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
di: Chen, Tan, et al.
Pubblicazione: (2024)

Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers
di: Zhang, Yue, et al.
Pubblicazione: (2026)

FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data
di: AbouNassar, Eman M., et al.
Pubblicazione: (2026)

FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
di: Chen, Tan, et al.
Pubblicazione: (2025)

Uncertainty-Aware Explainable Federated Learning
di: Zhang, Yanci, et al.
Pubblicazione: (2025)

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
di: Lin, Zheng, et al.
Pubblicazione: (2025)

TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
di: Hu, Gangqiang, et al.
Pubblicazione: (2024)

SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
di: Zhang, Yuxin, et al.
Pubblicazione: (2024)

Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
di: Wang, Zijian, et al.
Pubblicazione: (2025)

Federated Variational Preference Alignment with Gumbel-Softmax Prior for Personalized User Preferences
di: Koo, Jabin, et al.
Pubblicazione: (2026)

FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks
di: Lin, Zheng, et al.
Pubblicazione: (2023)

EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
di: Wang, Shuo, et al.
Pubblicazione: (2023)

DFML: Decentralized Federated Mutual Learning
di: Khalil, Yasser H., et al.
Pubblicazione: (2024)

Federated Graph Learning with Structure Proxy Alignment
di: Fu, Xingbo, et al.
Pubblicazione: (2024)

Learn How to Query from Unlabeled Data Streams in Federated Learning
di: Sun, Yuchang, et al.
Pubblicazione: (2024)

Gradient Correction in Federated Learning with Adaptive Optimization
di: Chen, Evan, et al.
Pubblicazione: (2025)

Trust-free Personalized Decentralized Learning
di: Li, Yawen, et al.
Pubblicazione: (2024)

Federated Learning with Flexible Architectures
di: Park, Jong-Ik, et al.
Pubblicazione: (2024)

Federated Multi-Objective Learning
di: Yang, Haibo, et al.
Pubblicazione: (2023)

Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
di: Gao, Zhidong, et al.
Pubblicazione: (2024)

FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
di: He, Jialuo, et al.
Pubblicazione: (2024)

LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
di: Zhang, Yuxin, et al.
Pubblicazione: (2025)

FedImpro: Measuring and Improving Client Update in Federated Learning
di: Tang, Zhenheng, et al.
Pubblicazione: (2024)

Tackling Selfish Clients in Federated Learning
di: Augello, Andrea, et al.
Pubblicazione: (2024)

Federated Learning with Limited Node Labels
di: Tang, Bisheng, et al.
Pubblicazione: (2024)

Variational Bayes for Federated Continual Learning
di: Yao, Dezhong, et al.
Pubblicazione: (2024)

On Using Large-Batches in Federated Learning
di: Tyagi, Sahil
Pubblicazione: (2025)

Efficient Client Selection in Federated Learning
di: Marfo, William, et al.
Pubblicazione: (2025)

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
di: Zhang, Yuxin, et al.
Pubblicazione: (2024)