Taking Advantage of the Mistakes: Rethinking Clustered Federated Learning for IoT Anomaly Detection

Clustered federated learning (CFL) is a promising solution to address the non-IID problem in the spatial domain for federated learning (FL). However, existing CFL solutions overlook the non-IID issue in the temporal domain and lack consideration of time efficiency. In this work, we propose a novel approach, called ClusterFLADS , which takes advantage of the false predictions of the inappropriate global models, together with knowledge of temperature scaling and catastrophic forgetting to reveal distributional similarities between the training data (of different clusters) and the test data. Additionally, we design an efficient feature extraction scheme by exploiting the role of each layer in a neural network's learning process. By strategically selecting model parameters and using PCA for dimensionality reduction, ClusterFLADS effectively improves clustering speed. We evaluate ClusterFLADS using real-world IoT trace data in various scenarios. Our results show that ClusterFLADS accurately and efficiently clusters clients, achieving a 100% true positive rate and low false positives across various data distributions in both the spatial and temporal domains.