Khái niệm cốt lõi
Federated Co-Training enhances privacy in collaborative machine learning by sharing hard labels on an unlabeled dataset, improving privacy substantially while maintaining model quality.
Thống kê
Federated learning allows us to collaboratively train models without pooling sensitive data directly.
FEDCT shares hard labels on an unlabeled dataset to improve privacy substantially.
FEDCT achieves a test accuracy comparable to FEDAVG and DD while enhancing privacy.
Trích dẫn
"Sharing hard labels substantially improves privacy over sharing model parameters."
"FEDCT achieves a model quality comparable to federated learning while improving privacy."