Sparse-FCL: Sparse Federated Continual Learning for Evolving Mobile Edge Computing Environments

Mobile Edge Computing (MEC) leverages embedded devices to deliver low-latency and cost-effective intelligent services, such as autonomous driving. Federated learning allows MEC devices to collaboratively learn a global model without exposing private data. However, the dynamic real-world environments that MEC devices operate in can lead to catastrophic forgetting of previously learned knowledge. While continual learning has been applied in federated learning to retain knowledge about past data, there are significant barriers when extending it for evolving MEC environments due to unbearable communication and computation costs for resource-constrained devices. We address this by proposing Sparse-FCL, a federated continual learning framework that uses sparse training to reduce training overhead and improve model performance. Specifically, to retain the generalization knowledge of specific tasks, we introduce a progressive neuron selection via multi-device collaboration module, which gradually selects the key neurons that show importance on multiple devices in each continual learning task. In addition, we propose a task-adaptive topology exploration module, aiming to provide more pliable dynamic sparse training configurations for the scenarios of federated continual learning. Experiments on FCL benchmarks demonstrate Sparse-FCL's superior accuracy under high sparsity levels, and it also achieves reductions of 80.4%, 89.5%, and 19.7% in communication, computation, and storage overhead, respectively, compared to existing federated continual learning methods.