Enterprise-scale multimodal federated self-supervised pretraining for privacy-preserving hyperautomation in healthcare information systems

Healthcare enterprises need multimodal AI but face privacy, non-IID heterogeneity, dynamic participation, and weak auditability. We propose an enterprise-scale multimodal federated self-supervised pretraining framework for privacy-preserving hyperautomation. A TargetNet-free federated BYOL learns modality-agnostic encoders for clinical text, images, and device signals without raw data sharing, while reducing communication and supporting client join/leave. An SOA stack with ESB integrates BPMN orchestration, RPA, and human confirmation, binding model I/O to HL7 FHIR and DICOM. Governance adds drift and uncertainty monitoring, audit logs, and lineage. Experiments show robust gains under severe non-IID and edge constraints.