FedHR5.0: A Human-Centric Federated Learning Framework for Organisational Learning and Workforce Competency Development in Healthcare

Healthcare organisations face simultaneous pressure to digitalise their workforce, redesign learning processes and adopt artificial intelligence in support of human resource decisions, while protecting sensitive employee data and respecting professional dignity — pressures more acute in healthcare than in other knowledge-intensive sectors, and bearing on clinical and administrative cadres alike. Three research streams have approached this problem in isolation: federated learning in healthcare, privacypreserving HR analytics, and organisational learning theory engaging with AI as a partner in knowledge management. Federated learning has predominantly addressed clinical data rather than workforce competency data; HR analytics has not been developed at the intersection with federated infrastructures; organisational learning theory has rarely engaged with the constraints that federated, privacy-preserving learning imposes. We position our contribution explicitly at the three-way intersection. We introduce FedHR5.0, a conceptual framework for privacy-preserving organisational learning and workforce competency development in healthcare. FedHR5.0 combines federated learning, (ε, δ)-differential privacy and transparent governance with a five-module architecture — trust-based, adaptive learning, emotionally-intelligent assessment, ethical AI, and collaborative knowledge exchange. The framework extends the SECI dynamics of Nonaka and Takeuchi toward a generative-AI-aware GRAI cycle, in which tacit–explicit transitions are mediated by an explicit privacy budget that doubles as a trust-building instrument. The artefact is positioned as a Design Science Research improvement-type contribution. Empirical validation is scoped as future work and given a concrete pathway: a multi-hospital pilot, four operationally measurable evaluation axes (model accuracy, effective privacy budget, organisational readiness, worker participation) and a three-phase iterative protocol grounded in Design Science Research. For HR practice, the framework repositions learning and development as a strategic driver of human–AI collaboration and treats workforce participation as a structural design requirement, applicable to clinical and administrative cadres alike. For research, it offers a starting point for cumulative design knowledge at the emerging intersection of federated learning, organisational learning and human-centric HR analytics.