Collaborative learning between edge servers (e.g., base stations) and end devices (e.g., drones) enables simultaneous model training in web applications through knowledge sharing. The resulting models effectively reduce service latency. However, existing approaches either assume isomorphic models on edge servers and end devices or incur substantial transmission overhead when training. Moreover, edge servers are often unable to access data from end devices on time due to long-distance constraints or strict data privacy regulations. This paper proposes a Prototype Augmentation-based Edge-end Collaborative Learning method (PAECL). It simultaneously trains heterogeneous edge and end models in the absence of data on edge servers by transmitting only augmented class-wise feature vectors (prototypes), significantly reducing communication overhead compared to sharing models, data, or logits. Specifically, on end devices, prototype-implied latent knowledge is augmented via local prototype contrast and global prototype alignment. On edge servers, prototypes are further augmented to produce bounded virtual vectors by mixing them with random noise, and the augmented prototypes are then delivered to generative models to provide data during edge model training. Through simulations and field experiments, PAECL achieves the highest accuracy for edge and end models under limited training resources and reduces the transmission burden by at least 297 times compared to existing edge-end heterogeneous learning methods.

Edge-end collaborative learning trains models with exchanged knowledge through distributed interaction, alleviating the cloud's burden. Edge-end heterogeneous collaborative learning further enables edge servers and end devices to train models of different scales in parallel based on their computational capabilities. This technology supports various applications in different resource conditions and improves server resource utilization. However, implementing it is challenging due to heavy communication costs and high global costs (time and energy). To this end, this paper proposes a novel prototype-based edge-end heterogeneous collaborative learning method and an optimization algorithm, which improves model performance and reduces training costs. We first use prototypes to perform collaborative learning and analyze the convergence. Prototypes are computed as mean feature vectors from different classes. The aggregated prototypes help capture class information on end devices and generate data on edge servers. Then, we study how to determine prototype selection and edge association to minimize training time, energy consumption, and prototype approximation error under a limited reward budget, which is proven to be NP-hard. We split the original problem into two subproblems. The first is solved in the closed form. Through approximation and reformulation, the second is transformed into a submodular maximization problem with knapsack and matroid constraints. On this basis, we propose an approximation algorithm with a theoretical guarantee. Finally, by simulation and field experiments, our method takes 3.61% of communication costs to improve heterogeneous edge and end models' accuracy by at least 5.16% and 2.77% compared with five baselines. The proposed algorithm outperforms others by at least 9.78% in terms of global cost.