标题： 探索联邦学习在热力城市特征分割中的应用——集中式与分布式方法的比较 显示英文标题

标题： Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approaches

摘要： 联邦学习（FL）是一种通过分布式训练数据和多个参与方来训练共享机器学习（ML）模型的方法。FL允许绕过传统集中式机器学习（CL）的限制，如果由于隐私或技术限制无法共享或集中存储数据，则不需要参与方之间共享数据。参与方使用自己的训练数据在本地训练模型。本文研究了在现实场景中联邦学习的实际实现和有效性，特别关注无人机（UAV）基于的热图像，在城市环境中常见的热特征检测。数据的分布式特性自然存在，使其适合联邦学习应用，因为可以在两个德国城市获取图像。由于两个地点采集的数据分布和特征特性不同，该应用面临独特的挑战。本研究通过在真实部署场景中评估联邦学习算法，做出了几个关键贡献。我们在模型准确性、训练时间、通信开销和能耗等关键性能指标上，将几种联邦学习方法与集中式学习基线进行比较。本文还探讨了各种联邦学习工作流程，比较了客户端控制的工作流程和服务器控制的工作流程。本工作的研究成果为理解基于无人机成像的分割任务中联邦学习方法的实际应用和局限性提供了有价值的参考。 显示英文摘要

摘要： Federated Learning (FL) is an approach for training a shared Machine Learning (ML) model with distributed training data and multiple participants. FL allows bypassing limitations of the traditional Centralized Machine Learning CL if data cannot be shared or stored centrally due to privacy or technical restrictions -- the participants train the model locally with their training data and do not need to share it among the other participants. This paper investigates the practical implementation and effectiveness of FL in a real-world scenario, specifically focusing on unmanned aerial vehicle (UAV)-based thermal images for common thermal feature detection in urban environments. The distributed nature of the data arises naturally and makes it suitable for FL applications, as images captured in two German cities are available. This application presents unique challenges due to non-identical distribution and feature characteristics of data captured at both locations. The study makes several key contributions by evaluating FL algorithms in real deployment scenarios rather than simulation. We compare several FL approaches with a centralized learning baseline across key performance metrics such as model accuracy, training time, communication overhead, and energy usage. This paper also explores various FL workflows, comparing client-controlled workflows and server-controlled workflows. The findings of this work serve as a valuable reference for understanding the practical application and limitations of the FL methods in segmentation tasks in UAV-based imaging.