A Principled Approach to Data Valuation for Federated Learning

Contributions Estimation in Federated Learning: A Comprehensive ...

A higher Accuracy or R2 value of the model represents a higher data utility, and it reaches its maximum value when v(S)=1. The selected metric should accurately ...

innovation-cat/Awesome-Federated-Machine-Learning - GitHub

Federated Learning (FL) is a new machine learning framework, which enables multiple devices collaboratively to train a shared model without compromising ...

Validation Free and Replication Robust Volume-based Data Valuation

collaborative machine learning, federated learning, trusted data sharing, data mar- ... A principled approach to data valuation for federated learning. In Q. Yang ...

Research - Xu, Xinyi

Data valuation quantifies the contribution of each data point to the performance of a machine learning model. Existing works typically define the value of data ...

Data Banzhaf: A Robust Data Valuation Framework for Machine ...

A principled approach to data valuation for federated learning. In Federated Learning, pages. 153–167. Springer, 2020. Tianhao Wang, Yu Yang, and Ruoxi Jia ...

The Shapley Value in Machine Learning - UBC Computer Science

Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, and Dawn Song. A Principled Approach to Data Valuation for Federated Learning. In: Yang, Q., ...

Data Valuation and Detections in Federated Learning - AIModels.fyi

Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the ...

similar - arxiv-sanity

In the context of machine learning (ML), data valuation methods aim to equitably measure the contribution of each data point to the utility of an ML model. One ...

Tianhao Wang – - Scholars at Harvard

Data Valuation for Federated Learning: (1) Established a novel approach ... A Principled Approach to Data Valuation for Federated Learning. ○␣ Tianhao ...

Empirical Measurement of Client Contribution for Federated ...

Data valuation using reinforcement learning(DVRL). [35] is a meta-learning framework that jointly learns the data value and trains the primary model using ...

Contribution Evaluation in Federated Learning: Examining Current ...

CE is, at its core, a data valuation problem, yet FL minimizes/eliminates the exchange of data. The more a CE method burdens clients with calculations or the ...

‪Ruoxi Jia‬ - ‪Google Scholar‬

A Principled Approach to Data Valuation for Federated Learning. T Wang, J Rausch, C Zhang, R Jia, D Song. Federated Learning: Privacy and Incentive, 2020. 208 ...

A Framework for an Equitable Graph Data Evaluation - ACM FAccT

Moreover, computation of Shapley values in a federated learning ... We proceed by training the model using only the shared data. By doing ...

‪Jiachen T. Wang‬ - ‪Google 학술 검색‬

A principled approach to data valuation for federated learning. T Wang, J Rausch, C Zhang, R Jia, D Song. Federated Learning: Privacy and Incentive, 153-167, ...

Equitable Data Valuation Meets the Right to Be Forgotten in Model ...

ABSTRACT. The increasing demand for data-driven machine learning (ML) mod- els has led to the emergence of model markets, where a broker.

Affordable federated edge learning framework via efficient Shapley ...

A. Ghorbani, J. Zou, Data shapley: Equitable valuation of data for machine learning, in: Proc. of ICML, 2019. Y. Kwon ...

DeceFL: a principled fully decentralized federated learning framework

Traditional machine learning relies on a centralized data pipeline for model training in various applications; however, data are inherently fragmented. Such ...

Understanding global aggregation and optimization of federated ...

In federated learning, clients usually do not transfer training data with each other. Thus, some malicious clients change model parameters (e.g., weights/ ...

Data Valuation for Vertical Federated Learning: A Model-free and ...

Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance ...

10-719: Federated and Collaborative Learning, Fall 2023

To do so, novel approaches must be developed that improve the accuracy and efficiency of learning across siloed data; mitigate risk and protect data privacy and ...