Toward Sustainable Healthcare AI Infrastructure: A Lifecycle-Aware Federated Mobile Crowd Learning Framework

Healthcare AI is becoming continuous rather than episodic, and the carbon and e-waste costs of this shift are rarely priced into architectural decisions. Federated learning (FL) keeps sensitive data local but can increase infrastructure demand. We formulate Federated Mobile Crowd Learning (FMCL), a reuse-centric alternative to dedicated edge deployment in which federated training runs preferentially on already-deployed consumer devices. A lifecycle carbon-accounting model compares centralized cloud training (which moves raw data off-site), dedicated edge FL, and FMCL under marginal, calendar-time, and shared-infrastructure allocation of embodied carbon. FMCL has a lower modeled footprint than dedicated edge under all three conventions and in 93.9% of 20,000 sampled scenarios over eight uncertain parameters; against cloud training, it leads only under marginal allocation, and device wear alone can reverse that comparison. Against pre-existing edge capacity, FMCL has a lower modeled footprint in 16.9% of sampled scenarios. A preliminary experiment on MIT-BIH arrhythmia classification compared convergence under energy-aware and random client selection, without comparing architectures; the policies did not differ detectably in rounds to a fixed balanced-accuracy target, and both met it while recognizing only two of the five classes. Applied to eight healthcare deployment contexts, the analysis identifies where FMCL reduces the infrastructure burden and where a dedicated alternative is preferable. Deployment measurements of wear, availability, communication energy, and convergence are still required.