FedPath-MTL: A Federated Multi-Task Framework for Personalized Learning Pathways with Real-Record Predictive Evaluation

Abstract

Personalized learning pathways are difficult to support in distributed educational systems because learner records cannot always be centralized and one shared federated predictor may not represent heterogeneity across institutions, learning tasks, and individual learners. We propose FedPath-MTL, a federated multi-task framework with global, task-specific, and learner-specific parameters, a bounded future-error proxy, a dynamic task graph, reliability-aware aggregation, and a constrained primal–dual beam-search planner. We evaluate only the next-outcome predictive component on four public educational releases: ASSISTments 2009–2010 Combined, Junyi Academy 2018–2019, EdNet-KT1, and OULAD. Every method is evaluated both before and after the same validation-only temperature-scaling protocol, and AUC, negative log-likelihood (NLL), Brier score, and 15-bin expected calibration error (ECE) are reported. The pooled central comparator has the highest mean AUC on all four releases. FedPath-MTL AUC ranges from 0.531 to 0.580, and identically calibrated ECE ranges from 0.098 to 0.127; matched comparisons with FedAvg-style training do not show a uniform AUC or calibration advantage. Observed school identifiers define clients only for ASSISTments; the other releases use explicitly labeled simulation partitions. The public logs do not jointly identify actions, propensities, subsequent-learning rewards, resource catalogs, and institutional constraints, so no pathway-effectiveness or causal learning-gain claim is made. Software tests verify equation-level implementation of the planner but do not validate educational benefit or real-institution constraint satisfaction.