<![CDATA[Enterprise organizations increasingly rely on predictive analytics while facing strict data privacy requirements and fragmented data landscapes that limit centralized model training. This study addresses the challenge of enabling accurate and scalable predictive intelligence without exposing sensitive enterprise data by proposing a privacy-preserving federated learning framework implemented on SAP Business Technology Platform. The research problem focuses on balancing model performance, data security, and interoperability across distributed environments. A mixed-method approach is adopted, combining quantitative evaluation of model accuracy, convergence behavior, and communication efficiency with qualitative assessment of governance, trust, and deployment feasibility. The proposed framework integrates secure aggregation, decentralized training orchestration, and policy-driven data access controls to ensure privacy while maintaining analytical performance. Experimental results demonstrate that federated models achieve comparable accuracy to centralized approaches while significantly reducing data exposure risks and improving compliance alignment. The study introduces an architectural blueprint that enhances scalability, resilience, and cross-organizational collaboration. Findings contribute to both academic discourse on distributed machine learning and industry practices by establishing a structured approach to privacy-aware predictive analytics. The research concludes that federated learning on SAP BTP enables sustainable, secure, and enterprise-ready AI adoption, offering strategic value for organizations seeking to unlock insights from sensitive and distributed data ecosystems.]]>
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