Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Model Sharing

This paper presents a federated learning approach that enables multiple organizations to collaboratively train conformal prediction models while protecting against malicious participants and minimizing communication overhead through partial model sharing. The method is designed for distributed settings where data cannot be centralized, making it applicable to regulated industries requiring privacy-preserving AI development across institutions.

This paper presents a federated learning approach that enables multiple organizations to collaboratively train conformal prediction models while protecting against malicious participants and minimizing communication overhead through partial model sharing. The method is designed for distributed settings where data cannot be centralized, making it applicable to regulated industries requiring privacy-preserving AI development across institutions.

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