Federated Learning 3.0 represents a significant advancement in privacy-preserving artificial intelligence training across diverse regulatory environments. This innovative framework addresses the fundamental challenges of cross-jurisdictional compliance through a novel self-healing architecture built on blockchain technology. By implementing “data passports” that track regulatory context and permissions, the system enables real-time compliance monitoring and automatic selective forgetting when required by regulations. The architecture’s self-healing mechanism maintains model performance after data removal through compensatory re-weighting techniques, allowing AI systems to operate continuously during remediation events. Performance benchmarks demonstrate substantial improvements over traditional federated learning approaches in erasure speed, audit accuracy, and performance retention. The framework shows particular promise in healthcare applications, including rare disease research, pandemic response, and personalized medicine, with potential extensions to financial services and edge computing environments. This approach effectively resolves the “AI amnesia” problem identified by NIST, providing organizations with a practical solution for maintaining regulatory compliance while leveraging the benefits of globally distributed training data.
Keywords: blockchain data passports, cross-jurisdictional AI, federated learning, regulatory compliance, self-healing AI
