Cross-Domain DataOps with Federated Learning: Unlocking AI in Regulated and Consumer-Facing Systems

The increasing pace of artificial intelligence (AI), its developments in domains like healthcare, finance and consumer applications require data solutions that can be robust, privacy oriented, and regulation ready. In this paper, a crossdomain framework, DataOps united with Federated Learning (FL), is presented to overcome the issues of distributed data governance, model accuracy and operational agility. Our solutions address the need by deploying DataOps to secure federated learning systems to allow the co-working of regulated institutions with front-end consumer systems, utilizing continuous integration, automated testing, and data pipeline orchestration. We introduce our new architecture which guarantees the data locality, compliance with the regulations of data privacy (e.g., GDPR, CA, HIPAA) as well as the adaptive updates to the models to respond to the new data sources (will be distributed across silos). Testing on heterogeneous data sets illustrates better training efficiency, reduced latency to convergence of a model and also a stable performance in situations they are deployed into. These are the implications of the study results; FL-enabled DataOps pipelines have the potential to revolutionize how to operationalize AI at scale with full respect of data sovereignty and consumer confidence.