发表机构
Arab Academy for Science, Technology and Maritime Transport; Leiden University; Leiden University Medical Center（阿拉伯科学、技术与海事运输学院; 莱顿大学; 莱顿大学医学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对联邦描述性分析未被充分探索的问题，提出基于联邦MDL的FedSLIM框架及两种互补变体，通过引入相关指标评估联邦MDL挖掘，实验表明其能保持高质量压缩结构，减少搜索量，还能恢复全局信息模式，为隐私保护描述性分析奠定基础。
AI 中文摘要
联邦学习在预测建模方面取得了显著成功，但联邦描述性分析在很大程度上仍未得到探索。现有联邦模式挖掘方法主要基于支持度，未优化诸如最小描述长度（MDL）这样有原则的全局目标。我们引入了FedSLIM，这是首个基于联邦MDL的描述性模式挖掘框架。基于SLIM原则，它能在不共享原始事务的情况下跨分布式数据库协作优化紧凑模式模型。我们提出了两种互补变体，在不同部署假设下平衡隐私、通信和优化保真度。为评估联邦MDL挖掘，引入了保真度和发现导向指标。实验表明，两种变体在IID和非IID分区的多个真实世界数据集上，既能保持高质量压缩结构，又比集中式基线所需搜索量少几个数量级。还揭示了分布式MDL挖掘中的局部 - 全局发现差距，两种变体都能恢复所有独立局部模型中不存在的全局信息模式。这些结果为跨分布式数据孤岛的隐私保护描述性分析奠定了实用基础。
英文摘要
Federated learning has achieved considerable success for predictive modelling, yet federated descriptive analytics remains largely unexplored. Existing federated pattern mining approaches are predominantly support-based and do not optimise a principled global objective such as Minimum Description Length (MDL). We introduce FedSLIM, the first federated MDL-based framework for descriptive pattern mining. Building on the SLIM principle, FedSLIM enables collaborative optimisation of compact pattern models across distributed databases without sharing raw transactions. We propose two complementary variants that balance privacy, communication, and optimisation fidelity under different deployment assumptions. To evaluate federated MDL mining, we introduce fidelity and discovery-oriented metrics that quantify agreement with a centralised baseline and assess recovery of globally informative patterns. Experiments on multiple real-world datasets under IID and non-IID partitioning show that both variants preserve high-quality compression structure while requiring orders of magnitude less search than the centralised baseline. We further reveal a local-global discovery gap in distributed MDL mining, where globally compressive patterns may be undiscoverable through isolated local optimisation. Both variants recover globally informative patterns absent from all standalone local models, demonstrating the benefits of federated optimisation beyond independent local mining. These results establish federated MDL mining as a practical foundation for privacy-preserving descriptive analytics across distributed data silos.
Comments17 pages, 2 figures
