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Found 4 papers, 1 papers with code
Evaluating Membership Inference Attacks and Defenses in Federated Learning
Firstly, combining model information from multiple communication rounds (Multi-temporal) enhances the overall effectiveness of MIAs compared to utilizing model information from a single epoch.
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention.
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature
Federated learning allows multiple parties to collaborate in learning a global model without revealing private data.
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof
Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other.
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