Design Principles for Secure Blockchain-Enabled Federated Learning in Cyber-Physical Systems

Cyber-Physical Systems (CPS) have become the foundation of modern smart infrastructures, including healthcare, industrial automation, intelligent transportation, and smart cities. These environments generate large volumes of distributed data while demanding secure, privacy-preserving, and real-time decision-making. Federated Learning (FL) enables collaborative model training without sharing raw data, whereas Blockchain provides decentralized trust, transparency, and secure coordination among participating entities. Although several Blockchain-enabled Federated Learning frameworks have been proposed, many continue to experience limitations related to scalability, communication overhead, adaptive trust management, and resistance to malicious model updates. This paper presents a concise set of design principles for developing secure Blockchain-enabled Federated Learning architectures for Cyber-Physical Systems. The proposed principles emphasize decentralized learning, blockchain-based trust management, privacy preservation, secure model aggregation, adaptive security, and edge-cloud collaboration. A layered reference architecture is presented to illustrate secure interactions among CPS devices, edge nodes, blockchain networks, and federated learning components. Comparative analysis with representative approaches demonstrates that the proposed design principles improve scalability, communication efficiency, trust management, and privacy preservation, making them suitable for next-generation Cyber-Physical Systems.