Image de couverture de livre

CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wu, Hao, Zhang, Likun, Li, Shucheng, Xu, Fengyuan, Zhong, Sheng
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Data Valuation and Detections in Federated Learning
par: Li, Wenqian, et autres
Publié: (2023)

Single-Round Scalable Analytic Federated Learning
par: Bacellar, Alan T. L., et autres
Publié: (2025)

Privacy-Preserving Federated Learning via Dataset Distillation
par: Xu, ShiMao, et autres
Publié: (2024)

Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
par: Han, Xiao, et autres
Publié: (2021)

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization
par: Cha, Seohyeon, et autres
Publié: (2026)

2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
par: Sun, Yifan, et autres
Publié: (2024)

Data Valuation and Selection in a Federated Model Marketplace
par: Li, Wenqian, et autres
Publié: (2025)

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
par: Zheng, Shuyuan, et autres
Publié: (2025)

Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments
par: Sun, Pengcheng, et autres
Publié: (2025)

LLM-based Human-like Traffic Simulation for Self-driving Tests
par: Li, Wendi, et autres
Publié: (2025)

SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information
par: Zhou, Kaiye, et autres
Publié: (2024)

Federated Recommender System with Data Valuation for E-commerce Platform
par: Park, Jongwon, et autres
Publié: (2025)

Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach
par: Xu, Changxin, et autres
Publié: (2024)

Redefining Contributions: Shapley-Driven Federated Learning
par: Tastan, Nurbek, et autres
Publié: (2024)

Is Data Valuation Learnable and Interpretable?
par: Wu, Ou, et autres
Publié: (2024)

Scalable Property Valuation Models via Graph-based Deep Learning
par: Riveros, Enrique, et autres
Publié: (2024)

On the Volatility of Shapley-Based Contribution Metrics in Federated Learning
par: Geimer, Arno, et autres
Publié: (2024)

Federated Learning with Sample-level Client Drift Mitigation
par: Xu, Haoran, et autres
Publié: (2025)

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
par: Lee, Jung Hyun, et autres
Publié: (2023)

Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning
par: Ukaye, Asim, et autres
Publié: (2026)

Data Valuation by Fusing Global and Local Statistical Information
par: Zhou, Xiaoling, et autres
Publié: (2024)

LiveVal: Time-aware Data Valuation via Adaptive Reference Points
par: Xu, Jie, et autres
Publié: (2025)

FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data
par: Huang, Chenghao, et autres
Publié: (2024)

Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
par: Liao, Xinting, et autres
Publié: (2024)

HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
par: Chen, Qiyuan, et autres
Publié: (2026)

CCFC: Bridging Federated Clustering and Contrastive Learning
par: Liu, Jing, et autres
Publié: (2024)

Enhancing Cross-Problem Vehicle Routing via Federated Learning
par: Meng, Xiangchi, et autres
Publié: (2026)

Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
par: Jia, Renqi, et autres
Publié: (2025)

Fast-DataShapley: Neural Modeling for Training Data Valuation
par: Sun, Haifeng, et autres
Publié: (2025)

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
par: Xu, Zhangchen, et autres
Publié: (2024)

History-Aware and Dynamic Client Contribution in Federated Learning
par: Ghosh, Bishwamittra, et autres
Publié: (2024)

AstRL: Analog and Mixed-Signal Circuit Synthesis with Deep Reinforcement Learning
par: Guo, Felicia B., et autres
Publié: (2026)

FiCoTS: Fine-to-Coarse LLM-Enhanced Hierarchical Cross-Modality Interaction for Time Series Forecasting
par: Lyu, Yafei, et autres
Publié: (2025)

Federated Graph Learning for Cross-Domain Recommendation
par: Yang, Ziqi, et autres
Publié: (2024)

FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Data
par: Qiao, Nan, et autres
Publié: (2025)

Better Generative Replay for Continual Federated Learning
par: Qi, Daiqing, et autres
Publié: (2023)

DeRDaVa: Deletion-Robust Data Valuation for Machine Learning
par: Tian, Xiao, et autres
Publié: (2023)

Source-Free Cross-Domain Continual Learning
par: Furqon, Muhammad Tanzil, et autres
Publié: (2025)

Operator-Theoretic Framework for Gradient-Free Federated Learning
par: Kumar, Mohit, et autres
Publié: (2025)

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
par: Jiang, Xuefeng, et autres
Publié: (2024)