探索用于热成像城市特征分割的联邦学习——集中式与去中心化方法的比较
Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approaches
AI总结：
本文研究了联邦学习在基于无人机热成像的城市特征分割任务中的实际应用，比较了集中式与去中心化方法在准确率、训练时间、通信开销和能耗等指标上的表现，为FL在真实场景中的部署提供了参考。
摘要：
联邦学习（FL）是一种利用分布式训练数据和多个参与者来训练共享机器学习（ML）模型的方法。如果由于隐私或技术限制导致数据无法被集中共享或存储，FL允许绕过传统集中式机器学习（CL）的限制——参与者在本地使用其训练数据训练模型，而无需与其他参与者共享数据。本文研究了FL在真实场景中的实际实现和有效性，特别关注基于无人机（UAV）的热成像图像，用于城市环境中的常见热特征检测。由于在两个德国城市捕获的图像可用，数据的分布式特性自然产生，使其适用于FL应用。由于在两个地点捕获的数据具有非同分布和特征特性，该应用带来了独特的挑战。该研究通过在真实部署场景而非模拟中评估FL算法，做出了几项关键贡献。我们比较了几种FL方法与集中式学习基线在模型准确率、训练时间、通信开销和能耗等关键性能指标上的表现。本文还探讨了各种FL工作流，比较了客户端控制的工作流和服务器控制的工作流。本工作的研究结果为了解FL方法在基于UAV成像的分割任务中的实际应用和局限性提供了有价值的参考。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
