A Controlled Evaluation of Transfer Learning and Federated Meta-Learning for Privacy-Preserving Photonic Inverse Design

This paper presents a controlled empirical evaluation of three widely used learning paradigms—transfer learning, centralized model-agnostic meta-learning (MAML), and federated meta-learning—for few-shot photonic inverse design under structured physical domain shifts and privacy constraints. Using a large-scale synthetic dataset of 500,000 physics-consistent grating coupler simulations, the study constructs a non-IID learning scenario by partitioning data across federated clients based exclusively on a physically meaningful parameter (grating period). All methods are evaluated within a unified experimental framework that enforces identical neural architectures, optimization settings, data splits, and statistically stabilized evaluation protocols, enabling a fair and reproducible comparison. Results show that conventional transfer learning achieves the lowest absolute prediction error and strong zero-shot generalization across period-shifted regimes. In contrast, both centralized and federated MAML exhibit limited few-shot adaptation, with modest improvements as the number of support samples increases, but consistently underperforming the transfer baseline in absolute terms. Importantly, federated meta-learning achieves performance parity with centralized meta-learning across all evaluated settings, with no statistically significant degradation despite operating without access to raw client data. Rather than proposing new algorithms, this work focuses on clarifying the practical behavior and limitations of established few-shot and privacy-preserving learning paradigms in a physics-driven inverse regression setting. By explicitly reporting both positive and negative results under tightly controlled conditions, the study provides a reproducible benchmark for future research in photonic inverse design, scientific machine learning, and federated learning under physically structured data heterogeneity.