Federated Learning-Driven Privacy-Preserving Framework for Decentralized Data Analysis and Anomaly Detection in Contract Review

Contract review is a critical legal task that involves several processes such as compliance validation, clause classification, and anomaly detection. Traditional, centralized models for the analysis of contracts raise significant data privacy and compliance challenges due to the highly sensitive nature of legal documents. This paper proposes a contract review-oriented federated learning framework, where model training can be performed in a completely decentralized way with data confidentiality. It leverages privacy preserving methods such as Differential Privacy (“DP”) and Secure Multi-Party Computation (“SMPC”) that provide protection for sensitive information during collaborative learning. The proposed framework reaches a clause classification accuracy of 94.2% while securing privacy requirements. Performance analysis of the training efficiency revealed that the federated model needed 13.1 hours instead of 10.4 hours for a centralized model while still protecting the security of the system. This research offers a scalable and secure approach toward contract review and offers a path forward for privacy-conscious AI-driven legal solutions.