  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.
