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.

翻译：深度学习模型因依赖中央服务器上的大规模数据集而引发了隐私和安全问题。随着物联网设备数量的增加，人工智能将在资源管理、数据处理和知识获取方面发挥关键作用。为解决这些问题，联邦学习提出了一种构建通用、大规模机器学习框架的新方法，该框架以去中心化且与硬件无关的方式运行。然而，联邦学习面临网络带宽限制和数据泄露的挑战。为减少联邦学习中的中央依赖并提高可扩展性，惠普企业联合提出了群体学习。群体学习代表一种去中心化的机器学习框架，利用区块链技术实现安全、可扩展且隐私保护的数据管理。基于区块链的网络使得参与者能够交换和聚合模型参数，从而降低单点故障风险并消除通信瓶颈。据我们所知，本综述首次系统介绍了群体学习的原理、架构设计及其应用领域。此外，文中还指出了学术与工业界需进一步探索的多个研究方向，以充分挖掘群体学习的潜力与应用价值。