去中心化时间序列分类，用火箭特征提升安全与鲁棒性
Decentralized Time Series Classification with ROCKET Features
- 采用无服务器的去中心化架构，通过节点间接力传递优化的卷积核
- 在UCR数据集上超越主流客户端-服务器联邦学习方法，准确率更高
- 适合注重隐私保护与抗攻击能力的医疗、金融等场景
时间序列分类（TSC）在医疗、金融和工业监控等领域具有重要意义。由于隐私和数据监管问题，联邦学习（FL）成为从分布式时间序列数据中学习而无需集中原始信息的有前景方案。然而，大多数FL方法依赖客户端-服务器架构，存在服务器角色过于突出的问题，易成单点故障，且可能泄露客户端知识。为此，我们提出DROCKS，一种完全去中心化的联邦学习框架，用于时间序列分类，利用ROCKET（RandOm Convolutional KErnel Transform）特征。在DROCKS中，全局模型通过结构化路径在联邦节点间顺序传播，每个节点优化模型并选择最有效的本地卷积核后传给下一节点。在UCR基准数据集上的大量实验表明，DROCKS优于现有最先进的客户端-服务器联邦学习方法，且对节点失效和恶意攻击更具鲁棒性。代码已公开于https://anonymous.4open.science/r/DROCKS-7FF3/README.md。
原文摘要 · Abstract (English)
Time series classification (TSC) is a critical task with applications in various domains, including healthcare, finance, and industrial monitoring. Due to privacy concerns and data regulations, Federated Learning has emerged as a promising approach for learning from distributed time series data without centralizing raw information. However, most FL solutions rely on a client-server architecture, which introduces robustness and confidentiality risks related to the distinguished role of the server, which is a single point of failure and can observe knowledge extracted from clients. To address these challenges, we propose DROCKS, a fully decentralized FL framework for TSC that leverages ROCKET (RandOm Convolutional KErnel Transform) features. In DROCKS, the global model is trained by sequentially traversing a structured path across federation nodes, where each node refines the model and selects the most effective local kernels before passing them to the successor. Extensive experiments on the UCR archive demonstrate that DROCKS outperforms state-of-the-art client-server FL approaches while being more resilient to node failures and malicious attacks. Our code is available at https://anonymous.4open.science/r/DROCKS-7FF3/README.md.
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