Federated Learning for Market Surveillance

Song, Philip; Kanwal, Summrina; Dashtipour, Kia; Gogate, Mandar

Abstract

The data utilized in market surveillance is highly sensitive; what may be available for machine learning is limited. In this paper, we examine how federated learning for time series data can be used to identify potential market abuse while maintaining client privacy and data security. We are interested in developing a time series-specific neural network employing federated learning. We demonstrate that when this strategy is used, the performance of detecting potential market abuse is comparable to that of the standard data centralized approach. Specifically, a non-federated model, a federated model, and a federated model with extra data privacy and security protection are evaluated and compared. Each model utilizes an LSTM autoencoder to identify market abuse. The results demonstrate that a federated model’s performance in detecting possible market abuse is comparable to that of a non-federated model. The optimum accuracy achieved was 0.86 by the non-federated model and 0.847 by the client 3 of the federated model with perturbation Moreover, a federated approach with extra data privacy and security experienced a slight performance loss but is still a competitive model in comparison to the other models. Although this approach results in increased privacy and security, there is a limit to how much privacy and security can be ensured, as excessive privacy led to extremely poor performance. Federated learning offers the ability to increase data privacy and security with little performance decrease.

• Online Publication Date: Feb 21, 2024
• Publication Date: 2024
• Deposit Date: May 21, 2024
• Publisher: Springer
• Pages: 199-218
• Book Title: Decision Making and Security Risk Management for IoT Environments
• ISBN: 978-3-031-47589-4
• DOI: https://doi.org/10.1007/978-3-031-47590-0_10