Cover Image

Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Crawshaw, Michael, Liu, Mingrui
Format:	Preprint
Published:	2024
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 AUC Maximization with Cyclic Client Participation
by: Vangapally, Umesh, et al.
Published: (2026)

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

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

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

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2025)

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

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

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

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

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

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

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

Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning
by: Li, Qingming, 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)

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

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

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

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

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

Convergence Analysis of Split Federated Learning on Heterogeneous Data
by: Han, Pengchao, et al.
Published: (2024)

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

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

CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
by: Zhang, Jun, et al.
Published: (2025)

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

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

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

Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
by: Xiang, Ming, et al.
Published: (2024)

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

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

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

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)

Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
by: Chadha, Mohak, et al.
Published: (2024)

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
by: Wang, Shuai, 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)

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

FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection
by: Geng, Jiaxiang, et al.
Published: (2024)

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

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