For remote sensing image scene classification tasks, the classification accuracy of the small-scale deep neural network tends to be low and fails to achieve accuracy in real-world application scenarios. However, although large deep neural networks can improve the classification accuracy of remote sensing image scenes to some extent, the corresponding deep neural networks also have more parameters and cannot be used on existing embedded devices. The main reason for this is that there are a large number of redundant parameters in large deep networks, which directly leads to the difficulty of application on embedded devices and also reduces the classification speed. Considering the contradiction between hardware equipment and classification accuracy requirements, we propose a collaborative consistent knowledge distillation method for improving the classification accuracy of remote sensing image scenes on embedded devices, called CKD. In essence, our method addresses two aspects: (1) We design a multi-branch fused redundant feature mapping module, which significantly improves the parameter redundancy problem. (2) To improve the classification accuracy of the deep model on embedded devices, we propose a knowledge distillation method based on mutually supervised learning. Experiments were conducted on two remote sensing image classification datasets, SIRI-WHU and NWPU-RESISC45, and the experimental results showed that our approach significantly reduced the number of redundant parameters in the deep network; the number of parameters decreased from 1.73 M to 0.90 M. In addition, compared to a series of student sub-networks obtained based on the existing different knowledge distillation methods, the performance of the student sub-networks obtained by CKD for remote sensing scene classification was significantly improved on two different datasets, with an average accuracy of 0.943 and 0.916, respectively.

Multiobject detection tasks in complex scenes have become an important research topic, which is the basis of other computer vision tasks. Considering the defects of the traditional single shot multibox detector (SSD) algorithm, such as poor small object detection effect, reliance on manual setting for default box generation, and insufficient semantic information of the low detection layer, the detection effect in complex scenes was not ideal. Aiming at the shortcomings of the SSD algorithm, an improved algorithm based on the adaptive default box mechanism (ADB) is proposed. The algorithm introduces the adaptive default box mechanism, which can improve the imbalance of positive and negative samples and avoid manually set default box super parameters. Experimental results show that, compared with the traditional SSD algorithm, the improved algorithm has a better detection effect and higher accuracy in complex scenes.

Under the federated learning framework, frequent parameter interactions between edge devices and servers result in communication inefficiency, while conventional encryption methods fail to resist multi-node collusion attacks. To address these challenges, this paper proposes an optimized federated learning scheme integrating adaptive channel pruning with multi-key homomorphic encryption. First, we construct a dynamic threshold determination mechanism that automatically calibrates channel pruning rates through precision feedback during the pre-pruning phase, achieving the optimal balance between model compression and accuracy, while significantly reducing communication bandwidth consumption compared to traditional algorithms. Second, based on the Brakerski-Gentry-Vaikuntanathan (BGV) multi-key fully homomorphic encryption architecture, we design a distributed public-key encryption protocol that enables aggregation servers to securely fuse multi-source model parameters without decryption, resisting collusion attacks from up to C − 1 nodes (where C denotes the total number of devices). Experiments on MNIST and CIFAR-10 datasets demonstrate that our scheme significantly reduces communication overhead through two complementary mechanisms: adaptive pruning reduces both the computational burden of local training and the volume of parameters transmitted per round, while multi-key BGV encryption ensures privacy-preserving aggregation without decryption. This work provides a novel technical pathway for privacy-preserving federated learning in resource-constrained scenarios.