Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models. Most existing valuation methods, however, are limited to single-task settings, overlooking scenarios where a buyer aims to optimize performance across multiple downstream tasks. Moreover, traditional valuation approaches, such as Shapley-based or retraining-based methods, are computationally expensive and poorly suited for decentralized environments without a trusted central coordinator and with strict privacy constraints. We propose DMVM (Decentralized Multi-task Valuation via Model Merging), a novel framework that bypasses retraining and data sharing by leveraging task arithmetic to infer dataset contributions directly from model combinations. Instead of retraining or sharing raw data, DMVM quantifies how models trained on different datasets combine in parameter space to infer each dataset's marginal utility across multiple tasks. This formulation yields a valuation process that is scalable, computationally efficient, and explicitly aligned with multi-task generalization behavior. To support decentralized deployment, we introduce a secure aggregation protocol that enables collaborative valuation without revealing individual model parameters or private data. We also provide theoretical error bounds characterizing the approximation quality of DMVM and validate our framework through comprehensive experiments on computer vision and natural language processing tasks.
Improving Fairness for Data Valuation in Horizontal Federated Learning
University of British Columbia（不列颠哥伦比亚大学）；Huawei Technologies Canada Co.（华为技术加拿大公司）；Simon Fraser University（西蒙弗雷泽大学）；KTH Royal Institute of Technology（KTH皇家理工学院）
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
University of Oxford（牛津大学）；University of Technology Nuremberg（纽伦堡工业大学）
Unconstrained Model Merging for Enhanced LLM Reasoning
The Hong Kong Polytechnic University（香港理工大学）；Zhejiang University（浙江大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Reallm Labs（Reallm实验室）；South China University of Technology（华南理工大学）；Simon Fraser University（西蒙菲莎大学）
