AIFed: Anomaly-resilient and Imbalance-aware Federated Learning

In geographically distributed systems, time series data often exhibit non-identical distributions (non-IID) across nodes due to diverse features and sample imbalances, posing significant challenges for centralized AI model training. The conventional federated learning paradigm, while mitigating data privacy and communication cost concerns, does not adequately address the imbalance in sample importance nor the presence of anomalous data in model training. This work introduces AIFed, an advanced federated learning framework designed to be both anomaly-resilient and imbalance-aware. AIFed judiciously assigns weights to client models based on data segment features, accounting for sample importance and anomaly levels. By innovatively incorporating local data characteristics into a global training process, AIFed significantly enhances model robustness and effectiveness in real-time learning scenarios. We demonstrate AIFed’s efficacy through extensive experiments that show improved performance over traditional federated learning approaches.