As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of potential abnormal behaviors and have limited means to assess the contributions of individual clients and implement the necessary intervention. Visualization techniques have been introduced to facilitate the HFL process inspection, usually by providing model metrics and evaluation results as a dashboard representation. Although the existing visualization methods allow a simple examination of the HFL model performance, they cannot support the intensive exploration of the HFL process. In this article, strictly following the HFL privacy-preserving protocol, we design an exploratory visual analytics system for the HFL process termed HFLens , which supports comparative visual interpretation at the overview, communication round, and client instance levels. Specifically, the proposed system facilitates the investigation of the overall process involving all clients, the correlation analysis of clients’ information in one or different communication round(s), the identification of potential anomalies, and the contribution assessment of each HFL client. Two case studies confirm the efficacy of our system. Experts’ feedback suggests that our approach indeed helps in understanding and diagnosing the HFL process better.

中文翻译：

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可视化分析横向联邦学习运行过程

作为一种去中心化的训练方法，水平联合学习 (HFL) 使分布式客户端能够协作学习机器学习模型，同时将个人/私人信息保存在本地设备上。尽管 HFL 的性能和效率高于本地训练，但由于 HFL 的隐私保护性质，通常缺乏检查参与客户和联合模型行为的线索。因此，用户只能对潜在的异常行为进行浅层分析，评估个体客户的贡献和实施必要干预的手段有限。引入可视化技术以促进 HFL 过程检查，通常通过提供模型指标和评估结果作为仪表板表示。尽管现有的可视化方法允许对 HFL 模型性能进行简单检查，但它们无法支持对 HFL 过程的深入探索。在本文中，我们严格遵循 HFL 隐私保护协议，为 HFL 流程设计了一个探索性可视化分析系统，称为HFLens ，它支持在概览、通信回合和客户端实例级别进行比较视觉解释。具体来说，所提出的系统有助于调查涉及所有客户的整个过程、在一个或多个通信轮次中对客户信息的相关性分析、潜在异常的识别以及每个 HFL 客户的贡献评估。两个案例研究证实了我们系统的有效性。专家的反馈表明，我们的方法确实有助于更好地理解和诊断 HFL 过程。