A differentially private equilibrium-seeking algorithm for OTA MIMO-based energy sharing protects prosumers' private data while converging to near-optimal solutions with quantified accuracy loss.
User- level privacy-preserving federated learning: Analysis and performance optimization
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Mechanism and Communication Co-Design for Differentially Private Energy Sharing
A differentially private equilibrium-seeking algorithm for OTA MIMO-based energy sharing protects prosumers' private data while converging to near-optimal solutions with quantified accuracy loss.
- Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation