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näkemys - Privacy-preserving Federated Learning Framework
A Game-theoretic Framework for Privacy-preserving Federated Learning Analysis
The authors propose a game-theoretic framework to address privacy concerns in federated learning, considering both defenders and attackers' payoffs. They introduce an oracle to provide lower and upper bounds of payoffs, facilitating the analysis of optimal strategies.
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy Analysis
The author proposes AerisAI, a decentralized collaborative AI framework, to address data privacy issues in federated learning by employing homomorphic encryption and differential privacy techniques.
Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacy
Proposing AerisAI for secure and efficient decentralized collaborative AI with differential privacy.
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