Limited Network

Limited network research focuses on optimizing data transmission and processing in bandwidth-constrained environments, aiming to improve efficiency and reliability for various applications. Current efforts concentrate on developing adaptive compression techniques (e.g., autoencoders), efficient distributed learning algorithms (e.g., federated learning with optimized scheduling and aggregation), and task-aware network coding that prioritizes relevant data for downstream processing. These advancements are crucial for enabling the deployment of AI and IoT technologies in resource-scarce settings, improving the performance of large-scale machine learning models, and facilitating remote control systems with limited uplink capacity.