Dynamic Bandwidth

Dynamic bandwidth research focuses on optimizing data transmission and processing in systems with fluctuating or limited bandwidth, aiming to improve efficiency and performance without sacrificing accuracy. Current research explores adaptive compression techniques, often employing neural networks (like variational recurrent networks or masked graph autoencoders) and machine learning algorithms (including multi-armed bandits and particle swarm optimization) to predict and manage bandwidth dynamically. These advancements are crucial for various applications, including federated learning, video streaming, and sensor networks, enabling efficient data handling in resource-constrained environments.