标题： 多层联邦学习中的编码空间属性 显示英文标题

标题： Encoded Spatial Attribute in Multi-Tier Federated Learning

摘要： 这项研究提出了一种编码空间多层联邦学习方法，用于对聚合模型在地理空间数据方面的全面评估。 在客户端层级，引入了编码空间信息以更好地预测目标结果。 本研究旨在评估这些模型在不同数据集和空间属性上的性能，突出预测准确性的差异。 使用准确性等评估指标，我们的研究揭示了空间粒度的复杂性和捕捉数据中潜在模式的挑战。 我们通过多层级以及编码空间属性的功能扩展了联邦学习（FL）的范围。 我们的N层FL方法使用编码的空间数据在不同层级进行聚合。 我们获得了能够预测空间数据不同粒度的多个模型。 我们的研究结果强调了进一步研究以提高预测准确性和模型泛化的必要性，潜在的研究方向包括引入更多特征、优化模型架构以及探索其他建模方法。 我们的实验有几个层级，代表不同的空间方面层次。 我们在不使用指定层级构成的数据训练模型的情况下，获得了全局模型的准确率分别为75.62%和89.52%。 该研究还突出了所提出方法在实时应用中的重要性。 显示英文摘要

摘要： This research presents an Encoded Spatial Multi-Tier Federated Learning approach for a comprehensive evaluation of aggregated models for geospatial data. In the client tier, encoding spatial information is introduced to better predict the target outcome. The research aims to assess the performance of these models across diverse datasets and spatial attributes, highlighting variations in predictive accuracy. Using evaluation metrics such as accuracy, our research reveals insights into the complexities of spatial granularity and the challenges of capturing underlying patterns in the data. We extended the scope of federated learning (FL) by having multi-tier along with the functionality of encoding spatial attributes. Our N-tier FL approach used encoded spatial data to aggregate in different tiers. We obtained multiple models that predicted the different granularities of spatial data. Our findings underscore the need for further research to improve predictive accuracy and model generalization, with potential avenues including incorporating additional features, refining model architectures, and exploring alternative modeling approaches. Our experiments have several tiers representing different levels of spatial aspects. We obtained accuracy of 75.62% and 89.52% for the global model without having to train the model using the data constituted with the designated tier. The research also highlights the importance of the proposed approach in real-time applications.