AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Experiments show that discriminative models with $n \geq 1$ consistently outperform federated Naive Bayes, and that privacy-preserving aggregation is effective with limited accuracy loss, establishing federated AnDE as a viable and privacy-preserving framework.
Experimental evaluation demonstrates the genetic algorithm's ability to obtain consensus BNs with limited treewidth, providing a valuable tool for aggregating information from diverse sources while returning a computationally actionable model.
This work proposes an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning.
The FedRCD family of causal discovery algorithms are introduced, and three variants that trade off communication rounds against algebraic noise are investigated, which effectively support exact unlearning at any granularity.
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