Federated Learning for Enterprise Information Systems: Security, Privacy, Architecture, and Deployment Challenges—A Review

Abstract

: Federated learning enables organizations to develop shared models while retaining local control of training records. Exchanged updates can nevertheless reveal sensitive information, making privacy protection a central system-design requirement. This narrative review examines cross-silo collaboration from the choice of data architecture to the operation of a shared prediction service. It compares differential privacy, secure aggregation, secure multi-party computation, homomorphic encryption, and trusted execution environments, explaining their protection boundaries and the costs of putting them into practice. Evidence from communication-efficient learning and encrypted-training systems makes the computational trade-offs concrete. Healthcare, credit-risk, and energy studies supply examples of cross-institutional prediction and its operating requirements. The synthesis distinguishes confidentiality during computation from disclosure through released outputs, and examines the tension between hidden updates and integrity checks. A seven-stage technical deployment framework turns these findings into a workflow for selecting protections, validating local performance, and maintaining the federation as its participants and services change. The review provides a basis for choosing complementary controls and planning deployment around both the learning task and its operational requirements.