FedGRAIL: Trustworthy and Efficient Federated Class-Incremental Learning for Long-Tailed IoT Edge Intelligence

In Internet of Things (IoT), artificial intelligence (AI) agents have become a core paradigm for edge intelligence, supporting on-device sensor data processing and privacy-preserving analytics. In practice, however, the sensor data streams evolve continuously and often exhibit nonindependent and identically distributed (non-IID) characteristics. Federated class-incremental learning (FCIL) has thus emerged as a promising approach to endow edge AI agents with long-tail learning capabilities, allowing models to adapt autonomously to persistent changes in the data. Despite recent progress, most existing FCIL methods assume balanced data distributions, which rarely hold in real-world deployments. Under realistic long-tailed distributions, the local optimization objectives of individual AI agents diverge from the desired global convergence, leading to head-class bias and rapid forgetting of tail classes. To overcome these limitations, we propose gradient-aligned and imbalance-aware FCIL model (FedGRAIL), a unified FCIL framework that resolves the conflict between local optimization bias and global consistency in dynamic IoT environments. FedGRAIL integrates local training and global aggregation within a structurally aligned learning process, rather than treating them as separate stages. It further introduces a gradient-correction mechanism that coordinates dispersed local optima and stabilizes global optimization via diversity-driven generative replay. This design supports effective retention of minority concepts while respecting the zero-storage constraints of edge agents. Extensive experiments on long-tailed benchmarks show that FedGRAIL substantially improves tail-class accuracy under both independent and identically distributed (IID) and non-IID conditions - achieving a 59% average gain over the strongest baseline, while preserving the competitive accuracy on head classes. These results demonstrate that FedGRAIL provides a storage-efficient, privacy-preserving, and robust solution for continual learning in IoT edge intelligence.