# 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. Android; communication protocol; federated learning; framework; machine learning; mobile +
Footnote †: journal: Medical Informatics  
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Footnote 1: email: _Applied_  
Footnote †: journal: Medical Informatics  
federated learning in emergency management applications is undergoing progressive expansion. For example, a privacy-preserving federated transfer learning approach for disaster classification (FedTL) has been proposed, which uses federated learning for disaster analysis from an image from social networks [7], or an application using the FedResilience algorithm that improves the resilience of resource-constrained critical infrastructures [8]. Even with these developments, the exploration of federated learning within emergency management applications on the Android platform still needs to be explored. While federated learning has shown promise in decentralized data utilization, its application and exploration in the Android ecosystem for emergency management remain limited. This paper introduces our proposed KI federated learning framework specifically proposed for emergency management applications and mainly our proposed communication protocol. The abbreviation KI is based on the Czech abbreviation for critical infrastructure. The framework and the communication protocol are designed to tackle the unique challenges presented by these systems, which include limited computational resources, diverse data sources, and rigorous security requirements. The framework incorporates novel communication protocols and machine learning techniques to facilitate collaborative model training while upholding data privacy. It empowers distributed machine learning model training across multiple Android devices, capitalizing on their collective intelligence without compromising the confidentiality of sensitive data. Furthermore, the framework features adaptive learning algorithms that can dynamically adjust model updates to accommodate the changing characteristics of the infrastructure system. This adaptive capability ensures that the trained models remain accurate and relevant in real-time operational scenarios. The scientific contribution is as follows:  
* Introduction of the KI Federated Learning Framework: Development of a novel framework, specifically designed for emergency management applications used with Android systems. * Innovative Communication Protocol: Proposal of a communication protocol compatible with Apache Kafka and Firebase, contributing to structured communication in federated learning systems. * Adaptive Learning Algorithms: Integration of adaptive learning algorithms to dynamically adjust model updates, ensuring relevance and accuracy in real-time operational scenarios. * Expansion of Federated Learning in Android: Exploration and expansion of federated learning applications within the Android platform, contributing to the evolving field of Android-based emergency management systems.