Imagen de Portada

Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Sen, Mrinmay, Aparna, Shruti, Agarwal, Rohit, Mohan, Chalavadi Krishna
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Distributed, Parallel, and Cluster Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization
por: Sen, Mrinmay, et al.
Publicado: (2025)

Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training
por: Lee, Sunwoo, et al.
Publicado: (2024)

Debiasing Federated Learning with Correlated Client Participation
por: Sun, Zhenyu, et al.
Publicado: (2024)

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning
por: Dhasade, Akash, et al.
Publicado: (2024)

GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
por: Seo, Jungwon, et al.
Publicado: (2025)

Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
por: Yang, Haibo, et al.
Publicado: (2024)

Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
por: Ying, Bicheng, et al.
Publicado: (2025)

Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning
por: Li, Qingming, et al.
Publicado: (2024)

Communication-Efficient Federated AUC Maximization with Cyclic Client Participation
por: Vangapally, Umesh, et al.
Publicado: (2026)

Accelerating Hybrid Federated Learning Convergence under Partial Participation
por: Bian, Jieming, et al.
Publicado: (2023)

FilFL: Client Filtering for Optimized Client Participation in Federated Learning
por: Fourati, Fares, et al.
Publicado: (2023)

Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
por: Crawshaw, Michael, et al.
Publicado: (2024)

Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology
por: Wu, Minghong, et al.
Publicado: (2025)

FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation
por: Howlader, S M Ruhul Kabir, et al.
Publicado: (2026)

Federated Graph Learning with Graphless Clients
por: Fu, Xingbo, et al.
Publicado: (2024)

Towards Client Driven Federated Learning
por: Li, Songze, et al.
Publicado: (2024)

FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
por: Zhou, Liuzhi, et al.
Publicado: (2024)

Federated Learning in the Presence of Adversarial Client Unavailability
por: Su, Lili, et al.
Publicado: (2023)

A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications
por: Guo, Wei, et al.
Publicado: (2024)

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
por: Fenoglio, Dario, et al.
Publicado: (2024)

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

Partial Federated Learning
por: Feng, Tiantian, et al.
Publicado: (2024)

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
por: Tian, Chunlin, et al.
Publicado: (2024)

Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
por: Balivada, Nihal, et al.
Publicado: (2026)

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
por: Wang, Xiaolu, et al.
Publicado: (2024)

Greedy Shapley Client Selection for Communication-Efficient Federated Learning
por: Singhal, Pranava, et al.
Publicado: (2023)

Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
por: Yang, Mengwei, et al.
Publicado: (2024)

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

Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach
por: Wang, Yanmeng, et al.
Publicado: (2025)

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

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
por: Duan, Moming, et al.
Publicado: (2021)

S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
por: Sánchez, Pedro Miguel Sánchez, et al.
Publicado: (2025)

Local Gradient Regulation Stabilizes Federated Learning under Client Heterogeneity
por: Luo, Ping, et al.
Publicado: (2026)

Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
por: Yuan, Liangqi, et al.
Publicado: (2024)

GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning
por: Na, Shijie, et al.
Publicado: (2024)

MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
por: Sun, Yuchang, et al.
Publicado: (2023)

Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
por: Li, Qiyue, et al.
Publicado: (2025)

A Unified Analysis of Federated Learning with Arbitrary Client Participation
por: Wang, Shiqiang, et al.
Publicado: (2022)

FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
por: Shen, Tao, et al.
Publicado: (2025)

TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
por: Wang, Mengdi, et al.
Publicado: (2024)