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A Principled Approach to Data Valuation for Federated Learning [article]

Tianhao Wang, Johannes Rausch, Ce Zhang, Ruoxi Jia, Dawn Song
2020 arXiv   pre-print
Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources.  ...  The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. It has been increasingly used for valuing training data in centralized learning.  ...  Hence, a new, principled approach to valuing data for FL is needed. In this paper, we propose the federated SV, a variant of the SV designed to appraise decentralized, sequential data for FL.  ... 
arXiv:2009.06192v1 fatcat:ba4jcnaxhraateifir6se4ft4u

Nature as capital: Advancing and incorporating ecosystem services in United States federal policies and programs: Table 1

Mark Schaefer, Erica Goldman, Ann M. Bartuska, Ariana Sutton-Grier, Jane Lubchenco
2015 Proceedings of the National Academy of Sciences of the United States of America  
We thank the many individuals in all sectors of society who have worked collaboratively to advance and apply ecosystem services concepts and practices, and we appreciate the constructive criticism and  ...  White House offices have committed to work with agencies to develop a federal research agenda to chart a course for closing knowledge gaps (15) .  ...  Despite progress in advancing the data infrastructure related to ecosystem services, considerable research is needed to further valuation approaches and the development of standard metrics and measures  ... 
doi:10.1073/pnas.1420500112 pmid:26082544 pmcid:PMC4475947 fatcat:uc6e2wax5jc4njvxvu2ibjmcvy

Data Valuation for Vertical Federated Learning: An Information-Theoretic Approach [article]

Xiao Han and Leye Wang and Junjie Wu
2021 arXiv   pre-print
Federated learning (FL) is a promising machine learning paradigm that enables cross-party data collaboration for real-world AI applications in a privacy-preserving and law-regulated way.  ...  How to valuate parties' data is a critical but challenging FL issue.  ...  In principle, data valuation for a VFL task should be mandatorily compliant with data protection regulations and should prevent the leakage of raw data from any parties.  ... 
arXiv:2112.08364v1 fatcat:w4zlx7mwzvbhdnlytm2xg6vrfu

Incentive Design and Differential Privacy based Federated Learning: A mechanism design Perspective

Sungwook Kim
2020 IEEE Access  
Due to stricter data management regulations and large size of the training data, distributed learning paradigm such as federated learning (FL) has gained attention recently.  ...  Based on the DP based incentive mechanism, our joint approach can leverage the full synergy that gives mutual advantages for users and learning operators.  ...  This approach can decouple the machine learning from acquiring, storing and training data in a central server.  ... 
doi:10.1109/access.2020.3030888 fatcat:vcqvjt77rzavrdjo34f6jx2pte

The Shapley Value in Machine Learning [article]

Benedek Rozemberczki, Lauren Watson, Péter Bayer, Hao-Tsung Yang, Olivér Kiss, Sebastian Nilsson, Rik Sarkar
2022 arXiv   pre-print
Then we give an overview of the most important applications of the Shapley value in machine learning: feature selection, explainability, multi-agent reinforcement learning, ensemble pruning, and data valuation  ...  Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning.  ...  Federated learning A federated learning scenario can be seen as a cooperative game by modeling the data owners as players who cooperate to train a high-quality machine learning model . Definition 14.  ... 
arXiv:2202.05594v1 fatcat:hr4ecxkvazfqzk5pmsp5e2mpbm

Improving Fairness for Data Valuation in Federated Learning [article]

Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, Yong Zhang
2022 arXiv   pre-print
[Federated Learning, 2020], is a measure for data value under the framework of federated learning that satisfies many desired properties for data valuation.  ...  Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy.  ...  In this paper, we develop a principled approach. The general idea is intuitive and effective.  ... 
arXiv:2109.09046v2 fatcat:wnh3wywbpndgpeeeb3ladpp7jm

Deriving adequate sample sizes for ANN-based modelling of real estate valuation tasks by complexity analysis

Sabine Horvath, Matthias Soot, Sebastian Zaddach, Hans Neuner, Alexandra Weitkamp
2021 Land Use Policy  
One approach which is enhancing the available data set is to evaluate these purchasing cases together with a neighbouring submarket. However, it leads to non-linearities.  ...  Consequently, nonlinear models for a cross-submarket real estate valuation are required to obtain reasonable results.  ...  The authors want to thank the authorities for granting the use of the data. Moreover, the authors acknowledge the TU Wien Bibliothek for financial support through its Open Access Funding Program.  ... 
doi:10.1016/j.landusepol.2021.105475 fatcat:so5wtdoexbaxjlkzjn3ptc3amq

Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning [article]

Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Cyril Leung, Chunyan Miao, Qiang Yang
2020 arXiv   pre-print
Furthermore, we present a case study involving optimal auction design using Deep Learning to price fresh data contributed for edge learning.  ...  This necessitates a paradigm shift from the current cloud-centric model training approach to the Edge Computing based collaborative learning scheme known as edge learning, in which model training is executed  ...  We then use a Deep Learning approach to ensure truthful reporting of the model owner's valuation amid information asymmetry.  ... 
arXiv:2006.00511v1 fatcat:7psh6ipc35a27k4jkibjlgzkna

Editorial for Special Issue "New Frontiers in Forecasting the Business Cycle and Financial Markets"

Alessia Paccagnini
2021 Forecasting  
Data Availability Statement: Not applicable. Conflicts of Interest: The author declares no conflict of interest.  ...  important role for structured product valuation.  ...  Castle, Doornik, and Hendry (2021) [6] study the general principles that seem to be the foundation for successful forecasting and show how these are relevant for methods that did well in the M4 competition  ... 
doi:10.3390/forecast3030030 fatcat:3zh4hglbpjdklbfy3cudltbuey

Citizen Science and Personal Data Protection

Andreas Matheus, Inian Moorthy, Linda See, Matej Batič, Steffen Fritz
2020 Zenodo  
LandSense, is a modern citizen observatory for Land Use & Land Cover (LULC) monitoring, connecting citizens with Earth Observation (EO) data to transform current approaches to environmental decision making  ...  This tiered approach provides the opportunity for operators to make applications and services available without the need to become a GDPR compliant data controller or processor if the service does not  ...  GDPR -Some Fundamental Aspects A great opportunity to establish a free movement of personal data (in Europe), but you don't get it for free!  ... 
doi:10.5281/zenodo.4017205 fatcat:jhc5f4kvq5erbfu7svojabmjni

Learning Automata Based Method for Grid Computing Resource Valuation with Resource Suitability Criteria

Ali Sarhadi
2011 International Journal of Grid Computing & Applications  
In this paper, we present a new method of resource allocation and valuation based on the learning automata algorithms in order to maximize the benefit for both grid providers and grid users.  ...  The essence of this problem is how to allocate and valuation resources for achieving the goal of a highly efficient utilization of resources in response to current resource valuations.  ...  The proposed approach uses learning automata for resource allocation with shortest time in executing of proffered application and then resource valuation based on its total time for executing of proffered  ... 
doi:10.5121/ijgca.2011.2401 fatcat:spvelutr6fhsfgyxc2hssg4ziy

Data Markets to support AI for All: Pricing, Valuation and Governance [article]

Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan
2019 arXiv   pre-print
For intrinsic value, we explain how to perform valuation of data in absolute terms (i.e just by itself), or relatively (i.e in comparison to multiple datasets) or in conditional terms (i.e valuating new  ...  We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data.  ...  with regards to a data valuation solution for data markets.  ... 
arXiv:1905.06462v1 fatcat:ya4vwfrievfj3ow4orlu4m7gey

A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data [article]

Lam Duc Nguyen, Shashi Raj Pandey, Soret Beatriz, Arne Broering, Petar Popovski
2021 arXiv   pre-print
An emerging paradigm in ML is a federated approach where the learning model is delivered to a group of heterogeneous agents partially, allowing agents to train the model locally with their own data.  ...  Extensive experimental evaluation of the proposed approach shows a competitive run-time performance, with a 15\% drop in the cost of execution, and fairness in terms of incentives for the participants.  ...  Langevoort, “Fraud and insider trading in american securities proach to data valuation for federated learning,” in Federated Learning, regulation: Its scope and philosophy in a global marketplace  ... 
arXiv:2112.02870v1 fatcat:ez7sl6z7lvdihapt6ldn4y572a

The future of the Australian valuation profession

Sara Wilkinson, Hera Antoniades, Dulani Halvitigala
2018 Property Management  
The research establishes a starting point for the profession from which to explore further.  ...  Finally, research is needed to gain a deeper understanding of these emerging trends and practices to ascertain whether different regions and markets are affected to greater or lesser extents.  ...  There is a notion that big data will eventually lead to less demand for the traditional valuation approach (Coester, 2015a) .  ... 
doi:10.1108/pm-04-2017-0026 fatcat:tompkybpmramfjzpwi5se7pjkm

FedMark: A Marketplace for Federated Data on the Web [article]

Tobias Grubenmann, Abraham Bernstein, Dmitry Moor, Sven Seuken
2018 arXiv   pre-print
To that end, we introduce two different approaches for deciding which data elements to buy and compare their performance.  ...  Our approach allows a customer to transparently buy data from a combination of different providers.  ...  In this paper, we proposed a new paradigm for funding these activities in the form of a market for data that combines a market-based approach with principles of federated querying.  ... 
arXiv:1808.06298v1 fatcat:k6ugnsetxvfdvpxqmpi5h3e7hm
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