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

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

ACM SIGSIM PADS 2025 - Proceedings of the 39th ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation

10.1145/3726301.3728403

https://dl.acm.org/doi/abs/10.1145/3726301.3728403

Bo Zhang, WT Cai, WJ Tan, Allan N Zhang

simulation

22 June 2025

Bo Zhang, Wen Jun Tan, Wentong Cai, and Allan N Zhang, "Synopsis: Privacy Meets Performance: Enhancing Distributed Simulation-based Federated Multi-agent Learning with Privacy-preserving Surrogate Model", SIGSIM-PADS '25: 39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Pages 81 - 82, 2025

This is an extended abstract for the conference. See text for the details.

Publisher Copyright

This research is supported by core funding from: SIMTech
Grant Reference no. : NA

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9798400715914

Singapore Institute of Manufacturing Technology