构建可插拔的联邦日志异常检测平台，支持自适应实验与复现。
FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection
- 模块化设计支持多种日志检测模型与聚合策略接入。
- 内置自监控、自配置与自适应控制能力，提升实验效率。
- 专为联邦学习场景优化，适合系统可靠性与隐私研究者使用。
基于日志的异常检测（LAD）对保障大规模分布式系统的可靠性至关重要。然而，现有大多数LAD方法假设集中式训练，常因隐私限制和日志分布特性而难以应用。联邦学习（FL）虽提供可行替代方案，但缺乏针对LAD需求定制的测试平台。为此，我们提出FedLAD——一个统一的训练与评估平台，支持在联邦学习约束下进行LAD模型的开发与测试。该平台支持多样化的LAD模型、基准数据集与聚合策略的即插即用，并提供运行时支持：验证日志记录（自监控）、参数调优（自配置）与自适应策略控制（自适应）。通过实现可复现且可扩展的实验，FedLAD弥合了主流联邦学习框架与日志异常检测需求之间的差距，为未来研究奠定基础。项目代码已公开：https://github.com/AA-cityu/FedLAD。
原文摘要 · Abstract (English)
Log-based anomaly detection (LAD) is critical for ensuring the reliability of large-scale distributed systems. However, most existing LAD approaches assume centralized training, which is often impractical due to privacy constraints and the decentralized nature of system logs. While federated learning (FL) offers a promising alternative, there is a lack of dedicated testbeds tailored to the needs of LAD in federated settings. To address this, we present FedLAD, a unified platform for training and evaluating LAD models under FL constraints. FedLAD supports plug-and-play integration of diverse LAD models, benchmark datasets, and aggregation strategies, while offering runtime support for validation logging (self-monitoring), parameter tuning (self-configuration), and adaptive strategy control (self-adaptation). By enabling reproducible and scalable experimentation, FedLAD bridges the gap between FL frameworks and LAD requirements, providing a solid foundation for future research. Project code is publicly available at: https://github.com/AA-cityu/FedLAD.
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