# A Proposal for a Federated Learning Protocol for Mobile and Management Systems
## 2 The State of the Art  
As part of our research [6], we present a comprehensive overview and comparative analysis of existing frameworks, along with an introduction to the fundamental principles of federated learning. Among the most prominent open-source federated learning frameworks are TensorFlow Federated (TFF) [9] and Federated AI Technology Enabler (FATE) [10]. TFF is tailored for decentralized data processing and boasts support for various algorithms such as FedAvg and FedSGD. It provides both a Federated Learning Application Programmable Interface (API) for high-level interfaces and a Federated Core (FC) API for distributed computing. TFF has demonstrated its efficacy in practical applications, such as mobile keyboard prediction [9]. FATE, on the other hand, places a strong emphasis on secure computing protocols based on homomorphic encryption and multiparty computation. It accommodates a broad spectrum of machine learning algorithms and traditional methods. Notable projects leveraging federated learning include VisionAir [11] for air quality estimation, an object recognition project by [PERSON] [12], and a privacy-preserving system for Android malware detection. The Flower project, built on TFF, facilitates federated learning across different servers and devices [13]. For addressing the challenge of heterogeneous data distribution, the Federated Learning with Dynamic Regularization (FedDyn) framework [14] has been developed. It tackles the balance between minimizing the error on individual devices and minimizing the global error by employing dynamic regularization to achieve convergence between local and global errors. Hybrid Federated Dual Coordinate Ascent (HyFDCA) [15] is dedicated to handling the intricacies of hybrid federated learning, where each client manages a specific subset of data samples or features. It utilizes a dual coordinate ascent approach to tackle these challenges. Lastly, the KafkaFed [16] framework introduces an algorithm for federated learning that harnesses Apache Kafka as its communication medium. KafkaFed leverages the robust messaging capabilities of Kafka to facilitate seamless communication and coordination among the participants in the federated learning process. These diverse frameworks offer a range of approaches and techniques to address the various aspects and challenges of federated learning. Emergency management applications encompass a wide array of essential systems and services crucial for a society and nation's proper functioning. Android federated learning can have significant applications and implications within the realm of emergency management applications. The integration of federated learning on Android devices within these applications offers several advantages, including improved privacy, localized data processing, and real-time decision-making capabilities. However, it is imperative to exercise meticulous consideration concerning security, resource constraints, data quality, and compliance to ensure the successful and responsible implementation of federated learning in the context of emergency management. Android's role in emergency management applications is an emerging field that has not received substantial attention thus far. Nonetheless, the Android operating system is gradually expanding beyond mobile devices and into the Internet of Things (IoT) domain. Consequently, it becomes increasingly crucial to explore the implications and potential applications of Android within emergency management systems. Furthermore, aside from the traditional deployment options, novel Android deployment solutions are emerging, such as Waydroid [17] and LineageOS [18]. These platforms enable the operation of a full-fledged Linux distribution with an Android compatibility layer on Android devices. This breakthrough enhances flexibility and customization, allowing users to leverage Android applications alongside Linux-based functionalities. These evolving deployment options open up exciting new possibilities for Android-based systems in a variety of contexts.