Federated Continual Learning for Privacy-Preserving Predictive Intelligence
Federated Learning (FL) enables privacy-preserving collaborative model training without sharing raw data but faces challenges in handling concept drift, non-IID data, and evolving tasks. Although Continual Learning (CL) supports lifelong knowledge adaptation and mitigates catastrophic forgetting, most existing approaches are designed for centralized environments. To address these limitations, this paper proposes the Federated Continual Learning framework for Privacy-Preserving Predictive Intelligence (FCL3Pi), which integrates federated optimization with continual learning to enable adaptive, decentralized, and privacy-preserving predictive intelligence. The framework incorporates decentralized model aggregation, local incremental learning, dynamic memory replay, adaptive regularization, and secure communication to improve learning under dynamic data distributions. It addresses key challenges including catastrophic forgetting, data heterogeneity, client drift, communication efficiency, scalability, and edge resource constraints. Experimental evaluation demonstrates improved prediction accuracy, knowledge retention, privacy preservation, communication efficiency, and convergence stability compared with conventional centralized learning, standalone continual learning, and traditional federated learning. The proposed framework provides a robust foundation for next-generation intelligent applications in healthcare, industrial automation, autonomous transportation, financial systems, smart cities, and large-scale IoT environments.