:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Pang, Jinlong, Wei, Jiaheng, Hua, Yifan, Qian, Chen, Liu, Yang
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	https://arxiv.org/abs/2506.16731
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
by: Li, Qiongxiu, et al.
Published: (2025)

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

FilFL: Client Filtering for Optimized Client Participation in Federated Learning
by: Fourati, Fares, et al.
Published: (2023)

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark
by: Zhang, Jianqing, et al.
Published: (2025)

Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)

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

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

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

BAFFLE: A Baseline of Backpropagation-Free Federated Learning
by: Feng, Haozhe, et al.
Published: (2023)

Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge
by: Li, Songyuan, et al.
Published: (2025)

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
by: Lin, Zheng, et al.
Published: (2025)

From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
by: Wu, Xinghao, et al.
Published: (2026)

Efficient Federated Learning with Timely Update Dissemination
by: Jia, Juncheng, et al.
Published: (2025)

Gradient Correction in Federated Learning with Adaptive Optimization
by: Chen, Evan, et al.
Published: (2025)

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

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

Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
by: Bao, Guangyin, et al.
Published: (2023)

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

Federated Learning with Limited Node Labels
by: Tang, Bisheng, et al.
Published: (2024)

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

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

DFML: Decentralized Federated Mutual Learning
by: Khalil, Yasser H., et al.
Published: (2024)

LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2025)

Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
by: Sun, Pengcheng, et al.
Published: (2024)

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

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

Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning
by: Chen, Sijia, et al.
Published: (2024)

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

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

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

When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
by: Zhuang, Weiming, et al.
Published: (2023)

FedCore: Straggler-Free Federated Learning with Distributed Coresets
by: Guo, Hongpeng, et al.
Published: (2024)

FedUV: Uniformity and Variance for Heterogeneous Federated Learning
by: Son, Ha Min, et al.
Published: (2024)

Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
by: Chen, Tan, et al.
Published: (2024)

Federated Learning with Flexible Architectures
by: Park, Jong-Ik, et al.
Published: (2024)

FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)

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

FedTrans: Efficient Federated Learning via Multi-Model Transformation
by: Zhu, Yuxuan, et al.
Published: (2024)

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

Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
by: Liu, Ruyue, et al.
Published: (2024)