:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Wu, Hao, Zhang, Likun, Li, Shucheng, Xu, Fengyuan, Zhong, Sheng
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2409.02495
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Data Valuation and Detections in Federated Learning
by: Li, Wenqian, et al.
Published: (2023)

Privacy-Preserving Federated Learning via Dataset Distillation
by: Xu, ShiMao, et al.
Published: (2024)

Single-Round Scalable Analytic Federated Learning
by: Bacellar, Alan T. L., et al.
Published: (2025)

Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
by: Han, Xiao, et al.
Published: (2021)

LLM-based Human-like Traffic Simulation for Self-driving Tests
by: Li, Wendi, et al.
Published: (2025)

CoreQ: Learning-Free Mismatch Correction and Successive Rounding for Quantization
by: Cha, Seohyeon, et al.
Published: (2026)

SwitchLoRA: Switched Low-Rank Adaptation Can Learn Full-Rank Information
by: Zhou, Kaiye, et al.
Published: (2024)

Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
by: Zheng, Shuyuan, et al.
Published: (2025)

Data Valuation and Selection in a Federated Model Marketplace
by: Li, Wenqian, et al.
Published: (2025)

2D-OOB: Attributing Data Contribution Through Joint Valuation Framework
by: Sun, Yifan, et al.
Published: (2024)

Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments
by: Sun, Pengcheng, et al.
Published: (2025)

Federated Recommender System with Data Valuation for E-commerce Platform
by: Park, Jongwon, et al.
Published: (2025)

Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach
by: Xu, Changxin, et al.
Published: (2024)

Redefining Contributions: Shapley-Driven Federated Learning
by: Tastan, Nurbek, et al.
Published: (2024)

Is Data Valuation Learnable and Interpretable?
by: Wu, Ou, et al.
Published: (2024)

Scalable Property Valuation Models via Graph-based Deep Learning
by: Riveros, Enrique, et al.
Published: (2024)

On the Volatility of Shapley-Based Contribution Metrics in Federated Learning
by: Geimer, Arno, et al.
Published: (2024)

Federated Learning with Sample-level Client Drift Mitigation
by: Xu, Haoran, et al.
Published: (2025)

Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning
by: Ukaye, Asim, et al.
Published: (2026)

Data Valuation by Fusing Global and Local Statistical Information
by: Zhou, Xiaoling, et al.
Published: (2024)

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
by: Lee, Jung Hyun, et al.
Published: (2023)

Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
by: Liao, Xinting, et al.
Published: (2024)

LiveVal: Time-aware Data Valuation via Adaptive Reference Points
by: Xu, Jie, et al.
Published: (2025)

FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data
by: Huang, Chenghao, et al.
Published: (2024)

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning
by: Xu, Zhangchen, et al.
Published: (2024)

History-Aware and Dynamic Client Contribution in Federated Learning
by: Ghosh, Bishwamittra, et al.
Published: (2024)

CCFC: Bridging Federated Clustering and Contrastive Learning
by: Liu, Jing, et al.
Published: (2024)

HO-SFL: Hybrid-Order Split Federated Learning with Backprop-Free Clients and Dimension-Free Aggregation
by: Chen, Qiyuan, et al.
Published: (2026)

Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
by: Jia, Renqi, et al.
Published: (2025)

Fast-DataShapley: Neural Modeling for Training Data Valuation
by: Sun, Haifeng, et al.
Published: (2025)

Enhancing Cross-Problem Vehicle Routing via Federated Learning
by: Meng, Xiangchi, et al.
Published: (2026)

Collapsing Sequence-Level Data-Policy Coverage via Poisoning Attack in Offline Reinforcement Learning
by: Zhou, Xue, et al.
Published: (2025)

FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models
by: Tan, Likun, et al.
Published: (2025)

FiCoTS: Fine-to-Coarse LLM-Enhanced Hierarchical Cross-Modality Interaction for Time Series Forecasting
by: Lyu, Yafei, et al.
Published: (2025)

FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Data
by: Qiao, Nan, et al.
Published: (2025)

DemoShapley: Valuation of Demonstrations for In-Context Learning
by: Xie, Shan, et al.
Published: (2024)

Federated Graph Learning for Cross-Domain Recommendation
by: Yang, Ziqi, et al.
Published: (2024)

Better Generative Replay for Continual Federated Learning
by: Qi, Daiqing, et al.
Published: (2023)

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
by: Jiang, Xuefeng, et al.
Published: (2024)

AstRL: Analog and Mixed-Signal Circuit Synthesis with Deep Reinforcement Learning
by: Guo, Felicia B., et al.
Published: (2026)