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Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
This vulnerability may lead the current heuristic hyperparameter configuration of SecureBoost to a suboptimal trade-off between utility, privacy, and efficiency, which are pivotal elements toward a trustworthy federated learning system.
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning
To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage.
ArcText: A Unified Text Approach to Describing Convolutional Neural Network Architectures
Data mining on existing CNN can discover useful patterns and fundamental sub-comments from their architectures, providing researchers with strong prior knowledge to design proper CNN architectures when they have no expertise in CNNs.
