A Proposal for a Federated Learning Protocol for Mobile and Management Systems

Abstract

In this research paper, we introduce a federated learning communication protocol tailored for emergency management applications. Our primary objective is to tackle the communication challenges that arise in such critical scenarios. In order to overcome the limitations associated with centralized server architectures, we present an innovative communication protocol. This protocol empowers the framework to effectively cooperate with multiple centralized servers, fostering efficient knowledge sharing and model training while ensuring the utmost data privacy and security. By harnessing this protocol, our framework elevates the performance and resilience of vital infrastructure systems operating on the Android platform, thereby facilitating real-time operational scenarios. This research makes a substantial contribution to the field of emergency management applications, as we offer a comprehensive solution that optimizes communication and enables seamless collaboration with numerous centralized servers.

Keywords

• Android
• communication protocol
• federated learning
• framework
• machine learning
• mobile