FedMIL：用于视频分析且具有优化 DPP 调度的联邦多示例学习
FedMIL: Federated-Multiple Instance Learning for Video Analysis with Optimized DPP Scheduling
AI总结：
本文提出 FedMIL 框架，结合联邦学习与多示例学习处理视频分析任务，并引入基于 DPP 的 DPPQ 客户端选择框架，在非独立同分布下以更少数据利用实现更优性能，适用于资源受限的边缘设备。
摘要：
包括交通监控系统在内的许多 AI 平台使用联邦学习（FL）进行去中心化传感器数据处理，在保护隐私的同时确保安全的信息传输。另一方面，将监督学习应用于高分辨率图像等大型数据样本，需要密集的人力来标注数据样本的不同部分。多示例学习（MIL）通过对分配给实例“包”的标签进行操作，缓解了这一挑战。本文介绍了联邦多示例学习（FedMIL）。该框架应用联邦学习来提升基于视频的 MIL 任务（例如使用分布式 CCTV 网络进行车辆事故检测）的训练性能。然而，去中心化环境中的数据源通常不是独立同分布（IID）的，这使得客户端选择变得势在必行，以便以最少的客户端共同代表整个数据集。为了应对这一挑战，我们提出了 DPPQ，这是一个基于行列式点过程（DPP）并带有基于质量的核的框架，用于选择具有最多样化数据集的客户端。与随机选择和当前基于 DPP 的客户端选择方法相比，即使在大多数非 IID 情况下使用较少的数据，也能实现更好的性能。这为在计算资源有限的边缘设备上部署提供了显著优势，为在庞大的智能传感器网络中训练 AI 模型提供了可靠的解决方案。
英文摘要：
Many AI platforms, including traffic monitoring systems, use Federated Learning (FL) for decentralized sensor data processing for learning-based applications while preserving privacy and ensuring secured information transfer. On the other hand, applying supervised learning to large data samples, like high-resolution images requires intensive human labor to label different parts of a data sample. Multiple Instance Learning (MIL) alleviates this challenge by operating over labels assigned to the 'bag' of instances. In this paper, we introduce Federated Multiple-Instance Learning (FedMIL). This framework applies federated learning to boost the training performance in video-based MIL tasks such as vehicle accident detection using distributed CCTV networks. However, data sources in decentralized settings are not typically Independently and Identically Distributed (IID), making client selection imperative to collectively represent the entire dataset with minimal clients. To address this challenge, we propose DPPQ, a framework based on the Determinantal Point Process (DPP) with a quality-based kernel to select clients with the most diverse datasets that achieve better performance compared to both random selection and current DPP-based client selection methods even with less data utilization in the majority of non-IID cases. This offers a significant advantage for deployment on edge devices with limited computational resources, providing a reliable solution for training AI models in massive smart sensor networks.
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。
