Performance Evaluation and Optimization Strategies for Privacy-Preserving Document Classification in Distributed Learning Environments

The proliferation of sensitive documents across healthcare, financial, and governmental sectors necessitates robust privacy-preserving classification mechanisms. This study presents a comprehensive performance evaluation of privacy-preserving document classification within distributed learning frameworks, examining federated learning and differential privacy implementations. Through systematic experimentation on benchmark datasets, we quantify accuracy-privacy trade-offs, communication overhead, and computational costs across various privacy budget configurations. Results demonstrate that adaptive privacy allocation reduces accuracy degradation by 12-18% compared to uniform distribution while maintaining equivalent privacy guarantees. Gradient compression techniques achieve 67% communication reduction with minimal convergence impact. These findings provide actionable deployment guidelines for organizations implementing privacy-preserving document processing systems.