Imagen de Portada

Gradient Correction in Federated Learning with Adaptive Optimization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Chen, Evan, Wang, Shiqiang, Zhang, Jianing, Han, Dong-Jun, Liu, Chaoyue, Brinton, Christopher
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
por: Zhang, Jianing, et al.
Publicado: (2025)

Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach
por: Liang, Dandan, et al.
Publicado: (2025)

FADAS: Towards Federated Adaptive Asynchronous Optimization
por: Wang, Yujia, et al.
Publicado: (2024)

Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task, Multi-Dialogue Settings
por: Yuan, Liangqi, et al.
Publicado: (2025)

Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
por: Oh, Yongjeong, et al.
Publicado: (2023)

Differentially-Private Multi-Tier Federated Learning
por: Chen, Evan, et al.
Publicado: (2024)

FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
por: Chen, Tan, et al.
Publicado: (2025)

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

Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
por: Zehtabi, Shahryar, et al.
Publicado: (2024)

Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity
por: Wu, Fei, et al.
Publicado: (2026)

FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
por: Zhang, Yuxin, et al.
Publicado: (2024)

Hubs and Spokes Learning: Efficient and Scalable Collaborative Machine Learning
por: Sharma, Atul, et al.
Publicado: (2025)

FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
por: He, Jialuo, et al.
Publicado: (2024)

Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?
por: Mei, Yongsheng, et al.
Publicado: (2024)

Uncertainty-Aware Explainable Federated Learning
por: Zhang, Yanci, et al.
Publicado: (2025)

Delayed Random Partial Gradient Averaging for Federated Learning
por: Hu, Xinyi
Publicado: (2024)

Federated Multi-Objective Learning
por: Yang, Haibo, et al.
Publicado: (2023)

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
por: Liu, Ji, et al.
Publicado: (2025)

Sparse Training for Federated Learning with Regularized Error Correction
por: Greidi, Ran, et al.
Publicado: (2023)

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

When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices
por: Zhang, Pengyu, et al.
Publicado: (2024)

PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
por: Li, Liangyan, et al.
Publicado: (2025)

AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
por: Lin, Zheng, et al.
Publicado: (2024)

Learn How to Query from Unlabeled Data Streams in Federated Learning
por: Sun, Yuchang, et al.
Publicado: (2024)

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
por: Liu, Ji, et al.
Publicado: (2024)

Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization
por: Chang, Zhan-Lun, et al.
Publicado: (2023)

SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning
por: Liu, Xinyang, et al.
Publicado: (2024)

A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments
por: Zhang, Ruirui, et al.
Publicado: (2024)

Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning
por: Danilenka, Anastasiya, et al.
Publicado: (2024)

Efficient Asynchronous Federated Learning with Sparsification and Quantization
por: Jia, Juncheng, et al.
Publicado: (2023)

FedImpro: Measuring and Improving Client Update in Federated Learning
por: Tang, Zhenheng, et al.
Publicado: (2024)

CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
por: Wu, Huiwen, et al.
Publicado: (2024)

Federated Graph Learning with Structure Proxy Alignment
por: Fu, Xingbo, et al.
Publicado: (2024)

Optimizing Federated Learning by Entropy-Based Client Selection
por: Lutz, Andreas, et al.
Publicado: (2024)

SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
por: Islam, Md Sirajul, et al.
Publicado: (2025)

Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models
por: Wu, Fei, et al.
Publicado: (2025)

FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
por: Pramanik, Anik, et al.
Publicado: (2026)

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

Fairness-Aware Job Scheduling for Multi-Job Federated Learning
por: Shi, Yuxin, et al.
Publicado: (2024)

Adaptive Consensus Gradients Aggregation for Scaled Distributed Training
por: Choukroun, Yoni, et al.
Publicado: (2024)