The deployment of federated learning (FL) frameworks in remote sensing imagery processing is becoming increasingly vital1. This project aims to implement and evaluate federated learning solutions for super-resolution (SR) across distributed clients, such as satellites and their ground stations, ensuring enhanced image resolution while adhering to data privacy standards. This proposal investigates various federated learning setups, including homogeneous and heterogeneous environments, alongside advanced techniques like model distillation2 to optimize performance. The primary objective is to establish a scalable federated system, where smaller edge SR models interact with a centralized robust SR model to achieve high-fidelity image reconstruction across diverse geographical datasets. We will determine the most effective strategies for deploying FL in remote sensing, balancing privacy, performance, and computational efficiency. Motivation and Goal: Remote sensing is critical for numerous applications, from environmental monitoring to urban planning3. Federated Learning (FL) offers a promising path to utilize super-resolution techniques while maintaining the privacy of geographically distributed data. In a federated setup, various remote sensing entities can collaboratively train SR models without sharing raw data, crucial under strict privacy regulations. We plan to use state-of-the-art FL frameworks, assessing both homogeneous (identical model architectures) and heterogeneous (varying models) environments. This exploration will focus on critical aspects: - Edge vs. Centralized Models: Assessing the integration of lightweight edge SR models with robust centralized models to ensure adaptability and efficient resource management. - Training Strategies: Investigating various training strategies like model distillation to enhance model performance efficiently. - Model Architecture Variability: Evaluating the impact of different model architectures on the system's overall performance and resource consumption. Our goal is to identify which federated learning configuration best supports super-resolution in remote sensing applications, with experiments designed to gauge both performance and scalability.