Secure FL Systems refer to Federated Learning (FL) setups that incorporate cryptographic techniques or privacy-enhancing technologies to protect data confidentiality. Federated Learning allows multiple parties to collaboratively train a machine learning model without directly sharing their raw data. Secure FL Systems add layers of protection, such as differential privacy or secure multi-party computation, to prevent data leakage during the training process.
Context
The development of secure FL systems is a significant area of research at the intersection of artificial intelligence, cryptography, and data privacy. Concerns regarding data sovereignty and regulatory compliance, like GDPR, drive the demand for these systems. Future applications in digital asset analytics, fraud detection, and personalized financial services will heavily rely on the ability to train models securely across distributed datasets without compromising sensitive user information.
ZKPoT consensus leverages zk-SNARKs to cryptographically validate a participant's model performance without revealing the underlying data or updates, unlocking scalable, private, on-chain AI.
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