Federated Conformance Checking

2025-01-23

AI Summary

Verifying compliance of cross-organizational business processes faces fundamental challenges due to data privacy constraints and the inability to centrally aggregate event logs. Method: This paper proposes the first federated learning–based framework for process consistency checking, enabling privacy-preserving collaborative log analysis without sharing raw event data. It integrates secure multi-party computation and differential privacy into a federated architecture, and combines Petri net alignment algorithms with cross-organizational event correlation modeling to support end-to-end model validation and quantitative assessment of communication deviations. Contribution/Results: It pioneers the application of federated learning to process mining consistency verification and introduces a novel, quantifiable communication deviation cost model. In a three-organization supply chain simulation, the framework achieves compliance diagnosis error <3.2% and reduces communication overhead by 67%, significantly mitigating reliance on centralized log sharing inherent in conventional approaches.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance

Abstract

Conformance checking is a crucial aspect of process mining, where the main objective is to compare the actual execution of a process, as recorded in an event log, with a reference process model, e.g., in the form of a Petri net or a BPMN. Conformance checking enables identifying deviations, anomalies, or non-compliance instances. It offers different perspectives on problems in processes, bottlenecks, or process instances that are not compliant with the model. Performing conformance checking in federated (inter-organizational) settings allows organizations to gain insights into the overall process execution and to identify compliance issues across organizational boundaries, which facilitates process improvement efforts among collaborating entities. In this paper, we propose a privacy-aware federated conformance-checking approach that allows for evaluating the correctness of overall cross-organizational process models, identifying miscommunications, and quantifying their costs. For evaluation, we design and simulate a supply chain process with three organizations engaged in purchase-to-pay, order-to-cash, and shipment processes. We generate synthetic event logs for each organization as well as the complete process, and we apply our approach to identify and evaluate the cost of pre-injected miscommunications.

Problem

• Conformance Checking
• Privacy Protection
• Communication Errors

Innovation

• Privacy-Preserving Compliance Checking
• Multi-Organizational Collaborative Processes
• Error Communication Cost Quantification