Blockchain Empowered Secure Collaboration for Swarm Robots: Storage and Computation

Abstract

In recent years, swarm robot systems have garnered increasing attention, both in the industry and academia. These collaborative systems demand effective solutions for data storage, sharing, and security to unlock their full potential. To address these needs, this paper introduces a comprehensive distributed storage and computation framework based on blockchain and federated learning technology. The framework enables real-time collaborative data storage and computation, ensuring the security and reliability of collective intelligence systems. For data storage, we combine blockchain and dynamic containers to achieve secure and efficient storage of diverse robot data. To facilitate secure data utilization and sharing among robots, we present a federated learning-based collaborative computation approach. It allows robots to exchange model parameters while safeguarding data security, providing a versatile collaborative computation framework for collective systems. To validate the security and resilience of our framework, we present a practical scenario involving multi-agent collaborative localization. We conduct a thorough evaluation of the performance and security of this collaborative localization system, offering valuable insights for researchers in the field of swarm robotics.