K-means, as a commonly used clustering method, has been widely applied in data analysis for smart meters. However, this method requires repeatedly computing the similarity between all data points and cluster centers in each iteration, which leads to high computational overhead. Moreover, the process of analyzing electricity consumption data by K-means can cause the leakage of users’ privacy, and the current differential privacy technique adopts a uniform privacy budget allocation for data, which reduces the availability of the data. In order to reduce the computational overhead of smart meter data analysis and improve data availability while protecting data privacy, this paper proposes an adaptive differential privacy-based CK-means clustering scheme, named DPCK. Firstly, we propose a CK-means method by improving K-means, which not only reduces the computation between data and centers but also avoids repeated computation by calculating the adjacent cluster center set and stability region for each cluster, thus effectively reducing the computational overhead of data analysis. Secondly, we design an adaptive differential privacy mechanism to add Laplace noise by calculating a different privacy budget for each cluster, which improves data availability while protecting data privacy. Finally, theoretical analysis demonstrates that DPCK provides differential privacy protection. Experimental results show that, compared to baseline methods, DPCK effectively reduces the computational overhead of data analysis and improves data availability by 11.3% while protecting user privacy.

Federated learning has emerged as a promising approach for privacy-preserving model training across decentralized UAV swarm systems. However, challenges such as data heterogeneity, communication constraints, and limited computational resources significantly hinder convergence efficiency in real-world scenarios. This work introduces a communication-aware federated learning framework that integrates multi-dimensional cost modeling with dynamic client aggregation. The proposed cost function jointly considers communication overhead, computation latency, and training contribution. A Shapley-inspired client evaluation mechanism is incorporated to guide aggregation by prioritizing high-impact participants. In addition, a two-phase training strategy is devised to balance learning accuracy and resource efficiency across different training stages. Experimental results on the MNIST and CIFAR-10 benchmark datasets under non-IID settings demonstrate that the proposed method achieves faster convergence, higher accuracy, and reduced communication-computation cost. These results highlight its suitability for deployment in bandwidth-constrained, resource-limited UAV edge environments.