GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
Fuente:
arXiv
Saved in:
| Main Authors: | Seo, Jungwon, Catak, Ferhat Ozgur, Rong, Chunming, Hong, Kibeom, Kim, Minhoe |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
by: Fourati, Fares, et al.
Published: (2023)
by: Fourati, Fares, et al.
Published: (2023)
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)
by: Islam, Md Sirajul, et al.
Published: (2024)
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
by: Kahenga, Ferdinand, et al.
Published: (2025)
by: Kahenga, Ferdinand, et al.
Published: (2025)
FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
Delayed Random Partial Gradient Averaging for Federated Learning
by: Hu, Xinyi
Published: (2024)
by: Hu, Xinyi
Published: (2024)
Analyzing the Impact of Participant Failures in Cross-Silo Federated Learning
by: Stricker, Fabian, et al.
Published: (2025)
by: Stricker, Fabian, et al.
Published: (2025)
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
by: Chen, Tan, et al.
Published: (2025)
by: Chen, Tan, et al.
Published: (2025)
Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks
by: Lee, Hunmin, et al.
Published: (2024)
by: Lee, Hunmin, et al.
Published: (2024)
FedFT: Improving Communication Performance for Federated Learning with Frequency Space Transformation
by: Palihawadana, Chamath, et al.
Published: (2024)
by: Palihawadana, Chamath, et al.
Published: (2024)
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
by: Su, Shangchao, et al.
Published: (2023)
by: Su, Shangchao, et al.
Published: (2023)
FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks
by: Xian, Youquan, et al.
Published: (2023)
by: Xian, Youquan, et al.
Published: (2023)
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
by: Zhai, Yijun, et al.
Published: (2024)
by: Zhai, Yijun, et al.
Published: (2024)
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
by: He, Yuepeng, et al.
Published: (2024)
by: He, Yuepeng, et al.
Published: (2024)
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
by: Shi, Hongrui, et al.
Published: (2024)
by: Shi, Hongrui, et al.
Published: (2024)
Tackling Selfish Clients in Federated Learning
by: Augello, Andrea, et al.
Published: (2024)
by: Augello, Andrea, et al.
Published: (2024)
Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)
by: Marfo, William, et al.
Published: (2025)
FedUV: Uniformity and Variance for Heterogeneous Federated Learning
by: Son, Ha Min, et al.
Published: (2024)
by: Son, Ha Min, et al.
Published: (2024)
FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
by: Pramanik, Anik, et al.
Published: (2026)
by: Pramanik, Anik, et al.
Published: (2026)
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
by: Seo, Jungwon, et al.
Published: (2025)
by: Seo, Jungwon, et al.
Published: (2025)
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
by: Zhang, Jianqing, et al.
Published: (2024)
by: Zhang, Jianqing, et al.
Published: (2024)
Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
by: Wang, Zijian, et al.
Published: (2025)
by: Wang, Zijian, et al.
Published: (2025)
Optimizing Federated Learning by Entropy-Based Client Selection
by: Lutz, Andreas, et al.
Published: (2024)
by: Lutz, Andreas, et al.
Published: (2024)
Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning
by: Barrak, Amine
Published: (2026)
by: Barrak, Amine
Published: (2026)
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
by: Sen, Mrinmay, et al.
Published: (2025)
by: Sen, Mrinmay, et al.
Published: (2025)
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
by: Wu, Huiwen, et al.
Published: (2024)
by: Wu, Huiwen, et al.
Published: (2024)
Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learning
by: Stępka, Ignacy, et al.
Published: (2025)
by: Stępka, Ignacy, et al.
Published: (2025)
FedMT: Federated Learning with Mixed-type Labels
by: Zhang, Qiong, et al.
Published: (2022)
by: Zhang, Qiong, et al.
Published: (2022)
FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)
by: Zhou, Pengzhan, et al.
Published: (2024)
FedTLU: Federated Learning with Targeted Layer Updates
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, et al.
Published: (2024)
Co-LoRA: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients
by: Seo, Minhyuk, et al.
Published: (2025)
by: Seo, Minhyuk, et al.
Published: (2025)
Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training
by: Lee, Sunwoo, et al.
Published: (2024)
by: Lee, Sunwoo, et al.
Published: (2024)
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
by: Hu, Gangqiang, et al.
Published: (2024)
by: Hu, Gangqiang, et al.
Published: (2024)
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024)
by: Gao, Zhidong, et al.
Published: (2024)
FedCore: Straggler-Free Federated Learning with Distributed Coresets
by: Guo, Hongpeng, et al.
Published: (2024)
by: Guo, Hongpeng, 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)
by: Howlader, S M Ruhul Kabir, et al.
Published: (2026)
Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning
by: Ukaye, Asim, et al.
Published: (2026)
by: Ukaye, Asim, et al.
Published: (2026)
PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
by: Li, Liangyan, et al.
Published: (2025)
by: Li, Liangyan, et al.
Published: (2025)
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
by: Chadha, Mohak, et al.
Published: (2024)
by: Chadha, Mohak, et al.
Published: (2024)
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
by: Abbasi, Ali, et al.
Published: (2024)
by: Abbasi, Ali, et al.
Published: (2024)
Similar Items
-
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
by: Fourati, Fares, et al.
Published: (2023) -
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024) -
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024) -
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
by: Kahenga, Ferdinand, et al.
Published: (2025) -
FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)