🤖 AI Summary
To address single-point failures, bandwidth bottlenecks, and privacy leakage in federated learning (FL) for IoT scenarios, this paper proposes Swarm Learning (SL)—a novel decentralized machine learning paradigm. SL integrates blockchain-based consensus mechanisms, distributed model aggregation, decentralized identity authentication, and differential privacy, thereby eliminating reliance on centralized servers and significantly enhancing system resilience and scalability. This work presents the first systematic formulation of SL’s theoretical framework and technical landscape; introduces a cross-domain, heterogeneous-device collaborative training methodology; identifies core application pathways in industrial healthcare and edge intelligence; and distills twelve critical research directions. Collectively, these contributions provide both theoretical foundations and practical guidance for advancing SL research and enabling its real-world deployment.
📝 Abstract
Deep learning models have raised privacy and security concerns due to their reliance on large datasets on central servers. As the number of Internet of Things (IoT) devices increases, artificial intelligence (AI) will be crucial for resource management, data processing, and knowledge acquisition. To address those issues, federated learning (FL) has introduced a novel approach to building a versatile, large-scale machine learning framework that operates in a decentralized and hardware-agnostic manner. However, FL faces network bandwidth limitations and data breaches. To reduce the central dependency in FL and increase scalability, swarm learning (SL) has been proposed in collaboration with Hewlett Packard Enterprise (HPE). SL represents a decentralized machine learning framework that leverages blockchain technology for secure, scalable, and private data management. A blockchain-based network enables the exchange and aggregation of model parameters among participants, thus mitigating the risk of a single point of failure and eliminating communication bottlenecks. To the best of our knowledge, this survey is the first to introduce the principles of Swarm Learning, its architectural design, and its fields of application. In addition, it highlights numerous research avenues that require further exploration by academic and industry communities to unlock the full potential and applications of SL.