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This paper proposes using multi-output regression to address the partial label ranking problem, introducing an encoder that, during the learning phase, transforms the (possibly incomplete) rankings with ties of labels to multivariate regression targets, an underexplored perspective in both LR and PLR.
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.
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.
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