Cover Image

Communication-Efficient Federated AUC Maximization with Cyclic Client Participation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Vangapally, Umesh, Wu, Wenhan, Chen, Chen, Guo, Zhishuai
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Communication-Efficient Federated Group Distributionally Robust Optimization
by: Guo, Zhishuai, et al.
Published: (2024)

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

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

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

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

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

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

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

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

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

Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
by: Sen, Mrinmay, et al.
Published: (2025)

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

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

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

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

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

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

FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
by: Ilhan, Fatih, et al.
Published: (2025)

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

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

CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning
by: Wang, Mengdi, et al.
Published: (2025)

Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
by: Marfo, William, et al.
Published: (2025)

RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients
by: Tran, Duc A., et al.
Published: (2025)

Towards Client Driven Federated Learning
by: Li, Songze, et al.
Published: (2024)

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

Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
by: Mujtaba, Ahmed, et al.
Published: (2025)

Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clients
by: Wu, Tian-Shuang, et al.
Published: (2026)

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

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

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
by: Li, Zilinghan, et al.
Published: (2023)

Communication and Computation Efficient Split Federated Learning in O-RAN
by: Gu, Shunxian, et al.
Published: (2025)

Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients
by: Zhang, Zikai, et al.
Published: (2024)

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

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

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

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

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

FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
by: Liang, Han, et al.
Published: (2024)

Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning
by: Ukaye, Asim, et al.
Published: (2026)

Communication-Efficient Federated Fine-Tuning
by: Theologitis, Michael, et al.
Published: (2025)