Lightweight Federated Unlearning for IoT Sensing Systems

Due to its practical integration with IoT networks and ability to remove specific data contributions from a trained model, federated unlearning (FUL) has become increasingly attractive for IoT sensing systems. However, existing FUL methods are inadequate for rollback (unlearning) in IoT sensing systems due to IoT devices' heterogeneity and the training process's dynamic nature. To address these issues, we propose “FedRollback”, a lightweight novel FUL method for efficient rollback in IoT sensing systems. The key idea is to compress and filter the retaining updates, dynamically adjust the adjustment retraining rounds, and weighted aggregation based on each user (IoT device) resource availability score. This approach reduces computational overhead, maintains model performance, mini-mizes storage overhead, and speeds up the rollback process by retraining the model in a few rounds based on adaptive intervals. Evaluations on four distinct datasets (i.e., MNIST, KMNIST, SVHN, and CelebA) show an approximately 11x average speed-up over retraining from scratch and 5x and 4x improvement over FedEraser and FedRemover, demonstrating its efficiency and robustness in real-world IoT data sensing systems.