Cross-F $^{2}$ SCIL: A Federated Few-Shot Class Incremental Learning Method for Cross Mobile Edge Network Environments

Edge Federated Learning (EFL) has demonstrated significant potential in the field of Artificial Intelligence of Things (AIoT) by protecting data privacy and reducing communication costs. However, in real-world scenarios, multiple independent edge networks seldom collaborate due to factors such as data heterogeneity and the absence of a central server. Mobile devices, acting as bridges across different environments, offer an opportunity to enable dynamic collaboration among multiple edge networks. Nevertheless, as mobile devices transition between edge networks, they may encounter new classes with only a few samples, leading to catastrophic forgetting of previous knowledge and overfitting in new environments. To address this challenge, we propose Cross-F $^{2}$ SCIL, a Federated Few-Shot Class Incremental Learning method that enables on-demand dynamic collaboration in mobile edge network environments. Cross-F $^{2}$ SCIL allows mobile devices to efficiently learn new class knowledge from few-shot samples upon entering new edge networks while consolidating prior knowledge to prevent forgetting. Specifically, to mitigate the forgetting caused by new class overwriting on devices and parameter dilution at the server, we design a two-phase training framework. In the first phase, we learn a local model using Prototype Augmentation to enhance the retention of prior knowledge. In the second phase, we obtain the global model via Hierarchical Personalized Parameter Aggregation to effectively integrate learned knowledge across devices. To effectively learn new class information while reducing overfitting, we incorporate Self-Supervised Knowledge Aggregation and Prototype Knowledge Fusion to enhance model generalization and seamlessly integrate new classes into the existing model. Compared to the best-performing baseline on each dataset, Cross-F $^{2}$ SCIL achieves an average improvement of 5.52% in Average Accuracy across five datasets, with the maximum improvement reaching 7.97%.