FedSLIM: Privacy-Preserving Federated MDL-Based Descriptive Pattern Mining Across Data Silos

arXiv (Cornell University)

Abstract

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.

Authors 3

1. Samar Samir Khalil Aachen Leiden Institute of Advanced Computer Science

   Leiden University · University of Applied Sciences Leiden · Arab Academy for Science, Technology, and Maritime Transport

   Affiliation as printed

   Computer Engineering Department , Arab Academy for Science , Technology and Maritime Transport , Alexandria 21913 , Egypt,

   Leiden Institute of Advanced Computer Science , Leiden University , 2333 CC Leiden , The Netherlands ,

2. Noha S. Tawfik

   Arab Academy for Science, Technology, and Maritime Transport

   Affiliation as printed

   Computer Engineering Department , Arab Academy for Science , Technology and Maritime Transport , Alexandria 21913 , Egypt,

3. Marco Spruit Aachen Department of Public Health and Primary Care Leiden Institute of Advanced Computer Science

   Leiden University · Leiden University Medical Center · University of Applied Sciences Leiden

   Affiliation as printed

   Department of Public Health & Primary Care , Leiden University Medical Center , 2333 ZA Leiden , The Netherlands

   Leiden Institute of Advanced Computer Science , Leiden University , 2333 CC Leiden , The Netherlands ,