Federated learning (FL) technology has significant advantages in solving data silos and user privacy problems, but the traditional federal average (FedAvg) algorithm is ineffective in classifying and faces the risk of refactoring attacks when dealing with non-independent and identically distributed (Non-IID) data, which is especially prominent since medical data involves sensitive personal health information. Therefore, optimizing FedAvg to adapt to Non-IID data distribution and enhancing privacy protection are urgent problems that need to be solved, and this paper investigates these two aspects. In order to enhance the classification performance of FedAvg under Non-IID distribution, this paper combines the optimized deep learning model SE-ResNet18-E with FedAvg to obtain the FedAvg(SE-ResNet18-E) algorithm. The algorithm takes advantage of the SE-ResNet18-E model in feature extraction and classification tasks, fully uses the data resources of each participant, and improves the classification performance of FedAvg under Non-IID distribution. In addition, the algorithm achieves high communication performance. Second, in order to enhance the security of FL in the medical domain, threshold Paillier encryption is further introduced on top of FedAvg(SE-ResNet18-E) to form the Safe-FedAvg(SE-ResNet18-E) algorithm, which solves the threat of reconstruction attack and private key leakage in medical FL. After experimental validation, the Safe-FedAvg (SE-ResNet18-E) algorithm effectively improves the accuracy of disease classification and effectively protects the privacy and security of medical data, and enhances the trust of medical organizations participating in FL.

Earthen ruins contain rich historical value. Affected by wind speed, temperature, and other factors, their survival conditions are not optimistic. Time series prediction provides more information for ruins protection. This work includes two challenges: (1) The ruin is located in an open environment, causing complex nonlinear temporal patterns. Furthermore, the usual wind speed monitoring requires the 10 meters observation height to reduce the influence of terrain. However, in order to monitor wind speed around the ruin, we have to set 4.5 meters observation height according to the ruin, resulting in a non-periodic and oscillating temporal pattern of wind speed; (2) The ruin is located in the arid and uninhabited region of northwest China, which results in accelerating aging of equipment and difficulty in maintenance. It significantly amplifies the device error rate, leading to duplication, missing, and outliers in datasets. To address these challenges, we designed a complete preprocessing and a Transformer-based multi-channel patch model. Experimental results on four datasets that we collected show that our model outperforms the others. Ruins climate prediction model can timely and effectively predict the abnormal state of the environment of the ruins. This provides effective data support and decision-making for ruins conservation, and exploring the relationship between the environmental conditions and the living state of the earthen ruins.