Model-based data markets: a multi-broker game theoretic approach
Model-based data markets: a multi-broker game theoretic approach
The application of machine learning (ML) in data analytics has become widespread across various fields, including healthcare, finance, marketing, and many others. Current research has extensively explored ML model markets, which are typically coordinated by a single broker acting as an intermediary between data providers and model buyers. However, this solution poses significant challenges due to the monopolistic control it grants the broker. Therefore, we propose a multi-broker market model to overcome these issues. The decision-making processes among multiple brokers are formulated as a multi-stage Stackelberg game designed to maximize the profits of each broker. Subsequently, the existence and uniqueness of nash Equilibrium for brokers' strategies are proven. We conduct simulation experiments using four types of cost functions for two brokers. The simulation results demonstrate that brokers can reach a Nash Equilibrium through the proposed market model.
Model Market, Stackelberg Game, Nash Equilibrium, Machine Learning as a Service
Ma, Yizhou
5553a466-fed9-47ff-9782-136504c98f9d
Jiang, Xikun
dce7982b-a4fa-43a0-8ba8-8b84ea04acdd
Wu, Evan W.
73411935-a317-475b-a056-3f8a93e4f18e
Ibáñez, Luis-Daniel
65a2e20b-74a9-427d-8c4c-2330285153ed
Shi, Jian
a0eec25e-831c-4b9a-a4a7-8335a8cea403
Ma, Yizhou
5553a466-fed9-47ff-9782-136504c98f9d
Jiang, Xikun
dce7982b-a4fa-43a0-8ba8-8b84ea04acdd
Wu, Evan W.
73411935-a317-475b-a056-3f8a93e4f18e
Ibáñez, Luis-Daniel
65a2e20b-74a9-427d-8c4c-2330285153ed
Shi, Jian
a0eec25e-831c-4b9a-a4a7-8335a8cea403
Ma, Yizhou, Jiang, Xikun, Wu, Evan W., Ibáñez, Luis-Daniel and Shi, Jian
(2024)
Model-based data markets: a multi-broker game theoretic approach.
In 23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications.
IEEE.
9 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
The application of machine learning (ML) in data analytics has become widespread across various fields, including healthcare, finance, marketing, and many others. Current research has extensively explored ML model markets, which are typically coordinated by a single broker acting as an intermediary between data providers and model buyers. However, this solution poses significant challenges due to the monopolistic control it grants the broker. Therefore, we propose a multi-broker market model to overcome these issues. The decision-making processes among multiple brokers are formulated as a multi-stage Stackelberg game designed to maximize the profits of each broker. Subsequently, the existence and uniqueness of nash Equilibrium for brokers' strategies are proven. We conduct simulation experiments using four types of cost functions for two brokers. The simulation results demonstrate that brokers can reach a Nash Equilibrium through the proposed market model.
Text
Paper_Multiple_brokers_TrustCom
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Accepted/In Press date: 1 November 2024
Venue - Dates:
23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, , Sanya, China, 2024-12-17
Keywords:
Model Market, Stackelberg Game, Nash Equilibrium, Machine Learning as a Service
Identifiers
Local EPrints ID: 496077
URI: http://eprints.soton.ac.uk/id/eprint/496077
PURE UUID: 495d26f1-33dc-4048-8f1b-fe6f87143a8d
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Date deposited: 03 Dec 2024 17:32
Last modified: 04 Dec 2024 03:22
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Contributors
Author:
Yizhou Ma
Author:
Xikun Jiang
Author:
Evan W. Wu
Author:
Luis-Daniel Ibáñez
Author:
Jian Shi
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