Federated Diversified Rule Discovery for Privacy-Preserving Cross-Enterprise Data Analytics

Abstract

Cross-enterprise data collaboration has become indispensable for uncovering systemic insights in sectors such as healthcare, finance, and supply chain management. Rule discovery, long valued for its interpretability and operational relevance, is challenged when data silos must remain physically and legally separate. Federated learning offers a privacy-conscious paradigm, yet federated rule mining introduces distinctive tensions between pattern coverage, rule diversity, privacy guarantees, and cross-organizational governance. This paper presents a system-oriented framework for federated diversified rule discovery that intertwines local pattern extraction, secure aggregation, and multi-objective diversification mechanisms derived from embedding-guided top-k discovery techniques. The discussion centers on architectural trade-offs, including the cost of communication against the richness of global rule sets, the balance between local autonomy and global consistency, and the integration of differential privacy with aggregation protocols. Diversification is examined as a structural device for reducing redundancy, elevating actionability, and distributing inferential power across participants. Governance, fairness, and regulatory alignment are woven into the analysis, emphasizing data sovereignty, incentive design, and the right to contest algorithmic outputs. Further investigation covers robustness under adversarial and heterogeneous conditions, sustainable deployment in resource-constrained environments, and the systemic policy implications of large-scale federated rule analytics. The work does not propose a specific algorithm but rather articulates a reference architecture and critical evaluation lens for constructing federated rule discovery pipelines that are simultaneously diverse, privacy-preserving, and institutionally sustainable.