Cover Image

On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zeng, Dun, Xu, Zenglin, Liu, Shiyu, Pan, Yu, Wang, Qifan, Tang, Xiaoying
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance
by: Zeng, Dun, et al.
Published: (2023)

Understanding Generalization of Federated Learning: the Trade-off between Model Stability and Optimization
by: Zeng, Dun, et al.
Published: (2024)

Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients
by: Wu, Zheshun, et al.
Published: (2023)

Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks
by: Wu, Zheshun, et al.
Published: (2023)

FedNoisy: Federated Noisy Label Learning Benchmark
by: Liang, Siqi, et al.
Published: (2023)

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

Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning
by: Pan, Sheng, et al.
Published: (2026)

IDInit: A Universal and Stable Initialization Method for Neural Network Training
by: Pan, Yu, et al.
Published: (2025)

FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data
by: Zhang, Yukun, et al.
Published: (2024)

FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
by: Shi, Changlong, et al.
Published: (2025)

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

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures
by: Wang, Jiacheng, et al.
Published: (2025)

FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data
by: Sun, Yuxia, et al.
Published: (2025)

Byzantine-Robust Federated Learning with Learnable Aggregation Weights
by: Parsa, Javad, et al.
Published: (2025)

FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
by: Peng, Zihao, et al.
Published: (2025)

An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
by: Wang, Yuan, et al.
Published: (2024)

FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making
by: Chen, Jiaxiang, et al.
Published: (2025)

ILoRA: Federated Learning with Low-Rank Adaptation for Heterogeneous Client Aggregation
by: Zhou, Junchao, et al.
Published: (2025)

Fisher-Informed Parameterwise Aggregation for Federated Learning with Heterogeneous Data
by: Chang, Zhipeng, et al.
Published: (2026)

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

FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
by: Wang, Xinpeng, et al.
Published: (2024)

AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning
by: Tang, Zehui, et al.
Published: (2026)

Selective Aggregation for Low-Rank Adaptation in Federated Learning
by: Guo, Pengxin, et al.
Published: (2024)

WinFLoRA: Incentivizing Client-Adaptive Aggregation in Federated LoRA under Privacy Heterogeneity
by: Kou, Mengsha, et al.
Published: (2026)

Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation
by: Khan, Muhammad Irfan, et al.
Published: (2023)

Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning
by: Wang, Lin, et al.
Published: (2024)

Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
by: Ranaweera, Kanishka, et al.
Published: (2025)

Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning
by: Wei, Chu-Yuan, et al.
Published: (2025)

FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching
by: Yan, Qifan, et al.
Published: (2025)

Entropy-driven Fair and Effective Federated Learning
by: Wang, Lin, et al.
Published: (2023)

FedAPA: Federated Learning with Adaptive Prototype Aggregation Toward Heterogeneous Wi-Fi CSI-based Crowd Counting
by: Guo, Jingtao, et al.
Published: (2025)

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
by: Fang, Zihan, et al.
Published: (2026)

FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
by: Guo, Yongxin, et al.
Published: (2023)

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
by: Yi, Liping, et al.
Published: (2024)

Federated Learning with Hypergradient-based Online Update of Aggregation Weights
by: Nakai-Kasai, Ayano, et al.
Published: (2026)

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

FedAgg: Adaptive Federated Learning with Aggregated Gradients
by: Yuan, Wenhao, et al.
Published: (2023)

Heterogeneous Graph Contrastive Learning with Meta-path Contexts and Adaptively Weighted Negative Samples
by: Yu, Jianxiang, et al.
Published: (2022)

PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
by: Qi, Shiyi, et al.
Published: (2024)

An Element-Wise Weights Aggregation Method for Federated Learning
by: Hu, Yi, et al.
Published: (2024)