Synopsis: Privacy Meets Performance: Enhancing Distributed Simulation-based Federated Multi-agent Learning with Privacy-preserving Surrogate Model

Authors

Allan N. Zhang (ORCID: https://orcid.org/0000-0003-4795-5843)
Wentong Cai (ORCID: https://orcid.org/0000-0002-0183-3835)
Wen Jun Tan (ORCID: https://orcid.org/0000-0003-2204-0639)
Bo Zhang (ORCID: https://orcid.org/0000-0002-3831-1109)

Institutions

Nanyang Technological University (SG)
Singapore Institute of Manufacturing Technology (SG)

Publication Details

Published: 2025-06-21
DOI: https://doi.org/10.1145/3726301.3728403
Primary Topic: Privacy-Preserving Technologies in Data
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Agency for Science, Technology and Research

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article

Synopsis: Privacy Meets Performance: Enhancing Distributed Simulation-based Federated Multi-agent Learning with Privacy-preserving Surrogate Model

Allan N. Zhang, Wentong Cai, Wen Jun Tan, Bo Zhang

Jun 21, 2025

Privacy-Preserving Technologies in Data

article

Synopsis: Privacy Meets Performance: Enhancing Distributed Simulation-based Federated Multi-agent Learning with Privacy-preserving Surrogate Model

Allan N. Zhang, Wentong Cai, Wen Jun Tan, Bo Zhang

June 21, 2025article en

Abstract

No abstract available for this paper.

Nanyang Technological University (SG), Singapore Institute of Manufacturing Technology (SG)

Agency for Science, Technology and Research

Openalex Percentile: Top 93%

Privacy-Preserving Technologies in Data

DOI Source OpenAlex

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