Privacy-Preserving Federated Learning for Finance: Challenges, Benchmarks, and Strategic Recommendations

Abstract

Federated learning (FL) offers a compelling solution to the privacy and compliance challenges that plague the financial industry by enabling decentralized machine learning without the need for raw data sharing. As regulations like GDPR and the GLBA enforce strict data protection requirements, financial institutions are increasingly exploring FL as an avenue for collaborative intelligence. This paper presents a comprehensive analysis of state-of-the-art FL algorithms tailored for finance, evaluates their performance across realistic tasks such as credit scoring, fraud detection, and customer segmentation, and identifies the trade-offs among performance, fairness, privacy, and communication cost. We benchmark twelve prominent FL algorithms, highlight their privacy implications with differential privacy and secure aggregation, and provide practical, strategic recommendations for deploying FL systems in financial environments. The study closes with a discussion on regulatory alignment, deployment challenges, and future directions for ethical and robust FL adoption in finance.