Swarm Learning: A Survey of Concepts, Applications, and Trends

Deep Learning (DL) has significantly advanced artificial intelligence (AI) across numerous applications. However, its foundational reliance on centralized data collection introduces critical limitations concerning privacy, security, and scalability. With the continued proliferation of the Internet of Things (IoT), massive volumes of sensitive data are generated at the network edge, necessitating collaborative learning systems capable of securely sharing information without compromising confidentiality. Federated Learning (FL) partially addresses these challenges by enabling on-device model training, but its dependence on a central coordinator remains a core vulnerability, creating communication bottlenecks, fairness constraints, and susceptibility to single-point-of-failure (SPOF) risks. Swarm Learning (SL), developed in collaboration with Hewlett Packard Enterprise (HPE), represents a decentralized advancement that mitigates these limitations by eliminating central orchestration. Through the integration of blockchain technology with distributed machine learning (ML), SL establishes a secure, transparent, and fault-tolerant paradigm for peer-to-peer model exchange and aggregation. This survey provides a comprehensive overview of the architectural foundations, enabling technologies, and applications of SL in sensitive domains, including healthcare, the Internet of Vehicles (IoV), and Industrial IoT. In addition, it introduces a comparative taxonomy that systematically categorizes existing research by scope, methodological approach, and evaluation maturity. The review concludes by identifying key research directions—such as lightweight consensus protocols, energy-efficient optimization, and cross-chain interoperability—to advance the development of practical, secure, and privacy-preserving SL systems.