The load features of ground motion are mainly reflected by three factors: amplitude, frequency, and duration. The combination of these factors determines the response of rock-soil mass and the structure safety under seismic load. By finite element method, this paper analyzes the influence of the three factors of ground motion on the dynamic response of a slope. The analysis shows that the slope displacement increased with the elevation from the bottom. The anti-dip fault puts the slope in an unfavorable deformation state. Due to the large residual deformation in the fault zone, a large displacement occurred on the slope top. It was also learned that the adjustment of amplitude only leads to proportional growth in the absolute value of the acceleration of the slope. Under the same conditions, the dynamic responses in different parts of the target slope are not greatly affected by the changing amplitude, but depend more on the material and spectral features of the rock-soil mass. The research results provide a reference for the evaluation and prediction of slope seismic stability and the evolution of slope damage under earthquakes with different frequencies, amplitudes, and durations.

As the scale of federated learning expands, solving the Non‐IID data problem of federated learning has become a key challenge of interest. Most existing solutions generally aim to solve the overall performance improvement of all clients; however, the overall performance improvement often sacrifices the performance of certain clients, such as clients with less data. Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning. In order to solve the above problem, the authors propose Ada‐FFL, an adaptive fairness federated aggregation learning algorithm, which can dynamically adjust the fairness coefficient according to the update of the local models, ensuring the convergence performance of the global model and the fairness between federated learning clients. By integrating coarse‐grained and fine‐grained equity solutions, the authors evaluate the deviation of local models by considering both global equity and individual equity, then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value, which can ensure that the update differences of local models are fully considered in each round of training. Finally, by combining a regularisation term to limit the local model update to be closer to the global model, the sensitivity of the model to input perturbations can be reduced, and the generalisation ability of the global model can be improved. Through numerous experiments on several federal data sets, the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.

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