Fed-ID: A Privacy-Preserving Federated Learning Framework for Cross-Institutional Synthetic Identity Discovery
Keywords:
Collaborative Detection, Distributed Learning, Fairness-Aware AI, Federated Learning, Fraud Detection, Privacy-Preserving Machine Learning, Synthetic Identity FraudAbstract
This research proposes Fed-ID, a privacy-preserving federated learning framework for cross-institutional detection of anomalous and synthetic identity patterns. Traditional centralized approaches are limited by regulatory constraints, data residency requirements, and single points of failure. Fed-ID employs a secure aggregation protocol enhanced with differential privacy to enable collaborative model training across distributed institutions without sharing raw data. A client-weighted optimization strategy addresses non-IID distributions across heterogeneous datasets, while a communication-efficient synchronization protocol minimizes bandwidth overhead. The framework further integrates a contrastive representation learning module to enforce identity consistency across federated nodes, improving generalization to unseen domains. Empirical evaluation on large-scale, multi-institution identity datasets demonstrates that Fed-ID achieves 98% parity with centralized baselines while maintaining formal privacy guarantees. These findings establish decentralized, collaborative AI as a practical and scalable approach for privacy-preserving identity verification across heterogeneous digital ecosystems.
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