Federated Learning-Based Legal Compliance Detection of Financial Information in Private Educational Institutions

To address the challenges of compliance detection under highly heterogeneous financial data and rapidly evolving regulations in private educational institutions, this paper proposes FedLC—a federated learning-based legal compliance detection framework. FedLC introduces a cross-institution adversarial feature alignment mechanism, integrates a dynamic legal knowledge graph with graph neural embeddings to enhance regulatory adaptability, and incorporates dual-gradient privacy optimization along with a risk feedback heatmap for interpretable modeling. Experiments conducted on real financial data from multiple educational institutions demonstrate that FedLC outperforms mainstream federated models in terms of F1-score, error rate, and communication cost, highlighting its strong practical applicability for deployment.