Decentralized P2P Federated Learning for Privacy-Preserving and Resilient Mobile Robotic Systems

Abstract

Swarms of mobile robots are being widely applied for complex tasks in various practical scenarios toward modern smart industry. Federated learning (FL) has been developed as a promising privacy-preserving paradigm to tackle distributed machine learning tasks for mobile robotic systems in 5G and beyond networks. However, unstable wireless network conditions of the complex and harsh working environment may lead to poor communication quality and bring big challenges to traditional centralized global training in FL models. In this article, a Peer-to-Peer (P2P) based Privacy-Perceiving Asynchronous Federated Learning (PPAFL) framework is introduced to realize the decentralized model training for secure and resilient modern mobile robotic systems in 5G and beyond networks. Specifically, a reputation-aware coordination mechanism is designed and addressed to coordinate a group of smart devices dynamically into a virtual cluster, in which the asynchronous model aggregation is conducted in a decentralized P2P manner. A secret sharing based communication mechanism is developed to ensure an encrypted P2P FL process, while a Secure Stochastic Gradient Descent (SSGD) scheme is integrated with an Autoencoder and a Gaussian mechanism is developed to ensure an anonymized local model update, communicating within a few neighboring clients. The case study based experiment and evaluation in three different application scenarios demonstrate that the PPAFL can effectively improve the security and resilience issues compared with the traditional centralized approaches for smart mobile robotic applications in 5G and beyond networks.