Explainable Federated Learning for Factory-Level Disease Burden Prediction in Bangladesh’s Ready-Made Garment Industry

This paper provides an empirical integration of federated learning (FedAvg and FedProx) with SHAP-based explainable AI for factory-level disease-burden-share regression using real multi-factory occupational health records from Bangladesh’s RMG sector.Key elements include client-partitioned (privacy-preserving) modeling across three factories that outperforms a matched centralized MLP baseline (≈14% lower macro TEST RMSE), held-out TEST SHAP analysis identifying Lag1_Burden_Share_01 as the dominant feature with relatively stable rankings across seeds, and demonstration that FedAvg and FedProx perform nearly identically in this setting. Results are specific to the three observed factories.