FedImpro: Measuring and Improving Client Update in Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Tang, Zhenheng, Zhang, Yonggang, Shi, Shaohuai, Tian, Xinmei, Liu, Tongliang, Han, Bo, Chu, Xiaowen |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning
by: Tang, Zichen, et al.
Published: (2024)
by: Tang, Zichen, et al.
Published: (2024)
DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization
by: Tang, Zhenheng, et al.
Published: (2025)
by: Tang, Zhenheng, et al.
Published: (2025)
Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
by: Pan, Xinglin, et al.
Published: (2025)
by: Pan, Xinglin, 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)
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
by: Liang, Han, et al.
Published: (2024)
by: Liang, Han, et al.
Published: (2024)
Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules
by: Pan, Xinglin, et al.
Published: (2024)
by: Pan, Xinglin, et al.
Published: (2024)
Fault-Tolerant Hybrid-Parallel Training at Scale with Reliable and Efficient In-memory Checkpointing
by: Wang, Yuxin, et al.
Published: (2023)
by: Wang, Yuxin, et al.
Published: (2023)
ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
by: Lin, Wenxiang, et al.
Published: (2026)
by: Lin, Wenxiang, et al.
Published: (2026)
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
by: Lin, Wenxiang, et al.
Published: (2025)
by: Lin, Wenxiang, et al.
Published: (2025)
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)
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
by: Ilhan, Fatih, et al.
Published: (2025)
by: Ilhan, Fatih, et al.
Published: (2025)
Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
by: Wu, Yebo, et al.
Published: (2024)
by: Wu, Yebo, et al.
Published: (2024)
FedCDC: A Collaborative Framework for Data Consumers in Federated Learning Market
by: Shi, Zhuan, et al.
Published: (2025)
by: Shi, Zhuan, et al.
Published: (2025)
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)
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)
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)
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)
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
by: Sun, Yuchang, et al.
Published: (2023)
by: Sun, Yuchang, et al.
Published: (2023)
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)
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)
FedFQ: Federated Learning with Fine-Grained Quantization
by: Li, Haowei, et al.
Published: (2024)
by: Li, Haowei, et al.
Published: (2024)
FedCostAware: Enabling Cost-Aware Federated Learning on the Cloud
by: Sinha, Aditya, et al.
Published: (2025)
by: Sinha, Aditya, et al.
Published: (2025)
FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning
by: Han, Pengchao, et al.
Published: (2023)
by: Han, Pengchao, et al.
Published: (2023)
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)
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
by: Ma, Mengmeng, et al.
Published: (2024)
by: Ma, Mengmeng, 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)
FedHC: A Hierarchical Clustered Federated Learning Framework for Satellite Networks
by: Liu, Zhuocheng, et al.
Published: (2025)
by: Liu, Zhuocheng, et al.
Published: (2025)
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)
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)
FedOptimus: Optimizing Vertical Federated Learning for Scalability and Efficiency
by: Shrivastava, Nikita, et al.
Published: (2025)
by: Shrivastava, Nikita, et al.
Published: (2025)
Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing
by: Luo, Yizhou, et al.
Published: (2024)
by: Luo, Yizhou, et al.
Published: (2024)
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025)
by: Seo, Jungwon, et al.
Published: (2025)
EcoFed: Efficient Communication for DNN Partitioning-based Federated Learning
by: Wu, Di, et al.
Published: (2023)
by: Wu, Di, et al.
Published: (2023)
FedFog: Resource-Aware Federated Learning in Edge and Fog Networks
by: Sobati-M, Somayeh
Published: (2025)
by: Sobati-M, Somayeh
Published: (2025)
FedTLU: Federated Learning with Targeted Layer Updates
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, 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)
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
by: Chellapandi, Vishnu Pandi, et al.
Published: (2024)
by: Chellapandi, Vishnu Pandi, et al.
Published: (2024)
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)
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
by: Peng, Hongyi, et al.
Published: (2024)
by: Peng, Hongyi, et al.
Published: (2024)
Similar Items
-
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning
by: Tang, Zichen, et al.
Published: (2024) -
DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization
by: Tang, Zhenheng, et al.
Published: (2025) -
Efficient MoE Inference with Fine-Grained Scheduling of Disaggregated Expert Parallelism
by: Pan, Xinglin, et al.
Published: (2025) -
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024) -
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
by: Liang, Han, et al.
Published: (2024)