Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

This paper presents a federated learning framework combining differential privacy with Byzantine-robust aggregation to enable secure collaborative model training across distributed systems in banking and healthcare. The approach addresses key challenges in regulated industries where data cannot be centralized: protecting individual privacy while defending against compromised or malicious participants in the training process.

This paper presents a federated learning framework combining differential privacy with Byzantine-robust aggregation to enable secure collaborative model training across distributed systems in banking and healthcare. The approach addresses key challenges in regulated industries where data cannot be centralized: protecting individual privacy while defending against compromised or malicious participants in the training process.

Financial services AI must satisfy both performance requirements and stringent explainability standards. QuettaMinds designs AI systems for finance clients that hold up under model risk management review.