A Review of Lightweight Multi-Party Computation and Federated Learning in Financial Systems
Tonsia Treesa Thomas, Heta Shukla · 2025
As the financial services are increasingly moving to the edge devices, safeguarding these sensitive transaction data without compromising on privacy has become a challenge. This systematic literature review analyzes peer-reviewed studies that explore lightweight federated learning (FL) techniques, gradient compression methods, and secure multi-party computation (MPC) protocols for privacy-preserving machine learning in financial systems. Our findings show that approaches like Federated Dropout (FedDrop), Quantized Stochastic Gradient Descent (QSGD), and Sparse Ternary Compression (STC) have been proposed to address communication overhead and device constraints. Furthermore, the review highlights privacypreserving frameworks having secure aggregation along with exploring lightweight MPC for distributed financial devices. Even with these advances, the real-world deployments within financial environments remain limited and act as gaps in privacy, communication cost, and model accuracy, especially for non-IID data. This timely review outlines the future research directions, like adaptive model compression and scalable secure aggregation frameworks for heterogeneous financial systems.