Mathematical Optimization Techniques in Hyperparameter Tuning for Large-Scale Federated Learning Networks with Privacy Constraints
Keywords:
Federated Learning, Hyperparameter Tuning, Privacy Constraints, Bayesian Optimization, Differential Privacy, Large-Scale Networks, Constrained OptimizationAbstract
Federated Learning (FL) is a decentralized machine learning paradigm that enables model training across distributed edge devices while preserving user privacy. However, hyperparameter tuning in large-scale FL networks remains challenging due to resource constraints, data heterogeneity, and strict privacy regulations. This paper explores mathematical optimization techniques for hyperparameter tuning in FL networks with privacy constraints. We examine gradient-free methods such as Bayesian Optimization (BO) and Evolutionary Algorithms (EAs), along with gradient-based techniques like Adaptive Momentum Estimation (Adam) and Differential Privacy-Aware Stochastic Gradient Descent (DP-SGD). Additionally, we discuss the trade-offs between convergence speed, model accuracy, and privacy guarantees. Our analysis highlights the importance of constrained optimization techniques, such as Lagrangian relaxation and dual optimization, in achieving efficient and privacy-preserving hyperparameter tuning. Experimental results demonstrate that hybrid approaches combining gradient-free search with privacy-aware optimizers outperform traditional methods in terms of both accuracy and privacy preservation.
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