Advancing Federated Machine Learning for Privacy-Preserving Financial Models: Performance Comparison with Standard Machine Learning on Financial Data

Aim/Purpose To explore the potential of Federated Machine Learning (FML) in developing predictive models while ensuring data privacy and security. Background The rise of data-driven technologies has led to an increased focus on privacy concerns associated with centralized data storage. FML offers a decentralized approach, allowing organizations to collaboratively train models without sharing sensitive data (McMahan et al., 2017). Methodology This study employs a FML framework, utilizing local model training on decentralized datasets, followed by aggregation of model updates to create a global model. Privacy-preserving techniques, such as differential privacy, are also implemented (Dwork & Roth, 2014). Contribution This research contributes to the field of machine learning by demonstrating the efficacy of FML in predictive modeling, highlighting its potential for secure and privacy-conscious applications. Findings The study indicates that FML can effectively enhance model performance while maintaining the privacy of individual data sources. Recommendations for Practitioners Practitioners are encouraged to adopt FML techniques in applications requiring high data security, particularly in sectors such as healthcare and finance. Recommendation for Researchers Future research should explore advanced aggregation methods and evaluate the scalability of FML in diverse settings. Impact on Society The findings of this research have implications for the broader application of machine learning in sensitive areas, promoting data privacy while harnessing the power of collaborative intelligence. Future Research Further investigations should focus on the robustness of FML against adversarial attacks and its applicability in real-world scenarios.