Edge-side Federated Continuous Learning Method Based on Brain-like Spiking Neural Networks

Mobile edge computing has become an important computing model adapted to the needs of smart IoT applications,with advantages such as low communication cost and fast service response.In practical application scenarios,on the one hand,the data acquired by a single device is usually limited;on the other hand,the edge computing environment is usually dynamic and variable.Aiming at the above problems,this paper focuses on edge federated continuous learning,which innovatively introduces spiking neural networks (SNNs) into the edge federated continuous learning framework to solve the catastrophic forgetting problem faced by local devices in dynamic edge environments while reducing the consumption of device computation and communication resources.The use of SNNs to solve the edge federated continuous learning problem faces two main challenges.First,traditional spiking neural networks do not take into account the continuously increasing input data,and it is difficult to store and update the knowledge over a long time span,which results in the inability to realize effective continuous learning.Second,there are variations in the SNN models learned by different devices,and the global model obtained by traditional federated aggregation fails to achieve a better performance on each edge device achieve better performance on each edge device.Therefore,a new spiking neural network-enhanced edge federation continuous learning (SNN-Enhanced Edge-FCL) method is proposed.To address challenge I,a brain-like continuous learning algorithm for edge devices is proposed,which employs a brain-like spiking neural network for local training on a single device,and at the same time adopts a sample selection strategy based on the flocking effect to save representative samples of historical tasks.To address challenge II,a global adaptive aggregation algorithm with multi-device collaboration is proposed.Based on the working principle of SNN,the spiking data quality index is designed,and through the data-driven dynamic weighted aggregation method to assign corresponding weights to different device models to enhance the generalization of the glo-bal model when the global model is aggregated.The experimental results show that compared with the edge federation continuous learning method based on traditional neural networks,the communication and computational resources consumed by the proposed method on the edge devices are reduced by 92%,and the accuracy of the edge devices on the test set for five continuous tasks is above 87%.