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

TL;DR

This survey introduces Swarm Learning, a decentralized AI framework leveraging blockchain to enhance privacy, scalability, and security in machine learning, especially for IoT and resource-constrained environments.

Contribution

It is the first comprehensive survey detailing the principles, architecture, and applications of Swarm Learning, highlighting future research directions.

Findings

Swarm Learning uses blockchain for secure model parameter exchange.

SL reduces central dependency and enhances scalability in distributed learning.

The survey identifies key research challenges and potential applications.

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.