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ідея - Privacy Protection in Federated Learning
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
The author aims to ensure strong privacy guarantees for Federated Learning under Data Reconstruction Attacks by constraining the transmitted information through innovative channel models and data space operations.
Federated Learning Privacy Protection Against Data Reconstruction Attacks
Constraining information leakage in Federated Learning against data reconstruction attacks is crucial for privacy protection.
Federated Learning: Defending Against Data Reconstruction Attacks
Ensuring privacy in Federated Learning against data reconstruction attacks is achievable by constraining transmitted information through controlled parameter channels and data space operations.
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