FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
Fuente:
arXiv
Saved in:
| Main Authors: | Liang, Han, Zhan, Ziwei, Liu, Weijie, Zhang, Xiaoxi, Tan, Chee Wei, Chen, Xu |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
by: Zhan, Ziwei, et al.
Published: (2024)
by: Zhan, Ziwei, et al.
Published: (2024)
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method
by: Jiang, Yu, et al.
Published: (2024)
by: Jiang, Yu, et al.
Published: (2024)
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications
by: Wilkins, Grant, et al.
Published: (2023)
by: Wilkins, Grant, et al.
Published: (2023)
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
by: Ilhan, Fatih, et al.
Published: (2025)
by: Ilhan, Fatih, et al.
Published: (2025)
FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
by: Wu, Zhiyuan, et al.
Published: (2023)
by: Wu, Zhiyuan, et al.
Published: (2023)
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
by: Feng, Zhiying, et al.
Published: (2023)
by: Feng, Zhiying, et al.
Published: (2023)
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
by: Ning, Zhiyuan, et al.
Published: (2024)
by: Ning, Zhiyuan, et al.
Published: (2024)
Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients
by: Zhang, Zikai, et al.
Published: (2024)
by: Zhang, Zikai, et al.
Published: (2024)
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)
by: Islam, Md Sirajul, et al.
Published: (2024)
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
by: Zhou, Liuzhi, et al.
Published: (2024)
by: Zhou, Liuzhi, et al.
Published: (2024)
Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
by: Wu, Yebo, et al.
Published: (2024)
by: Wu, Yebo, et al.
Published: (2024)
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
by: Duan, Moming, et al.
Published: (2020)
by: Duan, Moming, et al.
Published: (2020)
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
by: Shen, Tao, et al.
Published: (2025)
by: Shen, Tao, et al.
Published: (2025)
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024)
by: Jiang, Chutian, et al.
Published: (2024)
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
by: Tian, Chunlin, et al.
Published: (2024)
by: Tian, Chunlin, et al.
Published: (2024)
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
by: Duan, Moming, et al.
Published: (2021)
by: Duan, Moming, et al.
Published: (2021)
FedZero: Leveraging Renewable Excess Energy in Federated Learning
by: Wiesner, Philipp, et al.
Published: (2023)
by: Wiesner, Philipp, et al.
Published: (2023)
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
by: de Souza, Allan M., et al.
Published: (2024)
by: de Souza, Allan M., et al.
Published: (2024)
Pollen: High-throughput Federated Learning Simulation via Resource-Aware Client Placement
by: Sani, Lorenzo, et al.
Published: (2023)
by: Sani, Lorenzo, et al.
Published: (2023)
FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients
by: Jiang, Haocheng, et al.
Published: (2025)
by: Jiang, Haocheng, et al.
Published: (2025)
Personalized Federated Learning on Data with Dynamic Heterogeneity under Limited Storage
by: Tan, Sixing, et al.
Published: (2024)
by: Tan, Sixing, et al.
Published: (2024)
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2024)
by: Yi, Liping, et al.
Published: (2024)
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations
by: Zhu, Guogang, et al.
Published: (2024)
by: Zhu, Guogang, et al.
Published: (2024)
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning
by: Tastan, Nurbek, et al.
Published: (2024)
by: Tastan, Nurbek, et al.
Published: (2024)
Many Hands Make Light Work: Accelerating Edge Inference via Multi-Client Collaborative Caching
by: Liang, Wenyi, et al.
Published: (2024)
by: Liang, Wenyi, et al.
Published: (2024)
Debiasing Federated Learning with Correlated Client Participation
by: Sun, Zhenyu, et al.
Published: (2024)
by: Sun, Zhenyu, et al.
Published: (2024)
FedFisher: Leveraging Fisher Information for One-Shot Federated Learning
by: Jhunjhunwala, Divyansh, et al.
Published: (2024)
by: Jhunjhunwala, Divyansh, et al.
Published: (2024)
FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
by: Li, Zilinghan, et al.
Published: (2023)
by: Li, Zilinghan, et al.
Published: (2023)
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)
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
by: Yi, Liping, et al.
Published: (2023)
by: Yi, Liping, et al.
Published: (2023)
FedFQ: Federated Learning with Fine-Grained Quantization
by: Li, Haowei, et al.
Published: (2024)
by: Li, Haowei, et al.
Published: (2024)
Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
by: Li, Yi, et al.
Published: (2026)
by: Li, Yi, et al.
Published: (2026)
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2023)
by: Yi, Liping, et al.
Published: (2023)
Data Heterogeneity-Aware Client Selection for Federated Learning in Wireless Networks
by: Yang, Yanbing, et al.
Published: (2025)
by: Yang, Yanbing, et al.
Published: (2025)
FedLog: Personalized Federated Classification with Less Communication and More Flexibility
by: Yu, Haolin, et al.
Published: (2024)
by: Yu, Haolin, et al.
Published: (2024)
FedOptimus: Optimizing Vertical Federated Learning for Scalability and Efficiency
by: Shrivastava, Nikita, et al.
Published: (2025)
by: Shrivastava, Nikita, et al.
Published: (2025)
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by: Wu, Xinghao, et al.
Published: (2024)
by: Wu, Xinghao, et al.
Published: (2024)
pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
by: Kou, Wei-Bin, et al.
Published: (2024)
by: Kou, Wei-Bin, et al.
Published: (2024)
FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning
by: Li, Rukuo, et al.
Published: (2025)
by: Li, Rukuo, et al.
Published: (2025)
Similar Items
-
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
by: Zhan, Ziwei, et al.
Published: (2024) -
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method
by: Jiang, Yu, et al.
Published: (2024) -
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024) -
FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications
by: Wilkins, Grant, et al.
Published: (2023) -
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
by: Ilhan, Fatih, et al.
Published: (2025)