Quantum-Federated Learning: A review of frameworks and applications for Sustainable Development Goals (SDGs)

Abstract

The United Nations Sustainable Development Goals (SDGs) demand intelligent, inclusive, and ethically driven solutions. Artificial Intelligence (AI) is a disruptive technology in this space, but its application must be efficient and privacy-preserving. This review explores the integration of Quantum Computing (QC) with Federated Learning (FL) into a novel architecture: Quantum-Federated Learning (QFL). QFL facilitates decentralized, secure model training across clients like hospitals or smart grids without centralizing sensitive data. While FL alone faces computational limitations, QC addresses these through parallelism and high-speed optimization. The proposed QFL framework enables edge nodes to train quantum-enhanced models locally and share only encrypted updates with a central quantum aggregation server. We detail the QFL architecture—comprising quantum-enabled clients, a secure communication layer leveraging quantum cryptography, and a quantum server—and its workflow. The review critically analyzes how QFL can develop applications supporting specific SDGs: SDG 3 (privacy-preserving collaborative healthcare diagnostics), SDG 7 (optimized demand forecasting in decentralized smart grids), SDG 9 (secure predictive maintenance in industry), and SDG 13 (collaborative climate modeling without infringing data sovereignty). Major challenges such as hardware limitations, standardization, and quantum-security issues are discussed. The paper concludes that QFL represents a strategic milestone towards creating AI systems that are not only intelligent and high-performing but also ethical, reliable, and sustainable.