Cover Image

Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhou, Wujun, Ding, Shu, Li, ZeLin, Wang, Wei
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Continual Learning for Food Category Classification Dataset: Enhancing Model Adaptability and Performance
by: Bhattacharyya, Piyush Kaushik, et al.
Published: (2026)

Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
by: Ling, Chih Wei, et al.
Published: (2025)

Achieving Fairness Across Local and Global Models in Federated Learning
by: Makhija, Disha, et al.
Published: (2024)

Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
by: Zhou, Tailin, et al.
Published: (2023)

Communication-Efficient and Privacy-Adaptable Mechanism -- a Federated Learning Scheme with Convergence Analysis
by: Shiu, Chun Hei Michael, et al.
Published: (2026)

FedGuCci: Making Local Models More Connected in Landscape for Federated Learning
by: Li, Zexi, et al.
Published: (2024)

Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies
by: Guo, Yongxin, et al.
Published: (2023)

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
by: Tian, Yuxin, et al.
Published: (2024)

GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
by: Meerza, Syed Irfan Ali, et al.
Published: (2024)

Global and Local Prompts Cooperation via Optimal Transport for Federated Learning
by: Li, Hongxia, et al.
Published: (2024)

Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation
by: Li, Yiwei, et al.
Published: (2025)

Tree-based Models for Vertical Federated Learning: A Survey
by: Qian, Bingchen, et al.
Published: (2025)

Training Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification
by: Lieberman, Kelsey, et al.
Published: (2024)

Contrastive Continual Learning for Model Adaptability in Internet of Things
by: Chathoth, Ajesh Koyatan
Published: (2026)

Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning
by: Chen, Minghui, et al.
Published: (2024)

Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks
by: Lin, Yushen, et al.
Published: (2024)

PFed-Signal: An ADR Prediction Model based on Federated Learning
by: Li, Tao, et al.
Published: (2025)

Towards Performance-Enhanced Model-Contrastive Federated Learning using Historical Information in Heterogeneous Scenarios
by: Zhang, Hongliang, et al.
Published: (2026)

The Cost of Local and Global Fairness in Federated Learning
by: Duan, Yuying, et al.
Published: (2025)

Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing
by: Guo, Yongxin, et al.
Published: (2024)

Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting
by: Ou, Wenjie, et al.
Published: (2025)

Enhancing Model Interpretability with Local Attribution over Global Exploration
by: Zhu, Zhiyu, et al.
Published: (2024)

Decentralized Dynamic Cooperation of Personalized Models for Federated Continual Learning
by: Yang, Danni, et al.
Published: (2025)

FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model
by: Wu, Feijie, et al.
Published: (2024)

AdaWorld: Learning Adaptable World Models with Latent Actions
by: Gao, Shenyuan, et al.
Published: (2025)

FedNSAM:Consistency of Local and Global Flatness for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)

Optimized Local Updates in Federated Learning via Reinforcement Learning
by: Murad, Ali, et al.
Published: (2025)

Reliable, Adaptable, and Attributable Language Models with Retrieval
by: Asai, Akari, et al.
Published: (2024)

Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
by: Wang, Ruhan, et al.
Published: (2025)

FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning
by: Song, Changlin, et al.
Published: (2024)

One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models
by: Yang, Mingzhao, et al.
Published: (2023)

Theoretically Guaranteed Distribution Adaptable Learning
by: Xu, Chao, et al.
Published: (2024)

Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness
by: Li, Boyuan, et al.
Published: (2024)

Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models
by: Cai, Yifeng, et al.
Published: (2025)

Recovering Global Data Distribution Locally in Federated Learning
by: Yao, Ziyu
Published: (2024)

Enhancing Privacy in Federated Learning through Local Training
by: Bastianello, Nicola, et al.
Published: (2024)

FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
by: Yao, Yuan, et al.
Published: (2025)

Unlocking the Potential of Model Calibration in Federated Learning
by: Chu, Yun-Wei, et al.
Published: (2024)

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
by: Shao, Yumeng, et al.
Published: (2024)

Federated Learning on Non-iid Data via Local and Global Distillation
by: Zheng, Xiaolin, et al.
Published: (2023)