AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Secure Federated Learning protects user privacy by keeping local gradients within the ciphertext space, thereby preventing direct exposure of raw gradients during aggregation. Paradoxically, this very protection introduces a fundamental vulnerability: the opacity of encrypted gradients blinds the server, rendering hone...
The research findings suggest that improved federated learning can achieve an optimal predictive performance, privacy protection, and secure collaborative learning, which makes it a viable method for next-generation distributed AI systems.
Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequent...
This study addresses the challenges of privacy leakage and data silos in multi-source heterogeneous data interaction within smart grids by designing a federated multi-source data fusion architecture that combines adaptive local differential privacy with feature space alignment. This architecture utilizes Hessian matrix...
Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high computational cost while the application of differential privacy (DP) methods would sacrifice model performance. To establi...
A hybrid privacy-preserving scheme that integrates Threshold Fully Homomorphic Encryption (ThFHE) and Multi-Key Fully Homomorphic Encryption (MKFHE) and effectively achieves collaborative model training while maintaining security at the privacy protection level and supporting seamless vehicle mobility is proposed.
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