Communication Optimization

Communication optimization in distributed computing focuses on minimizing the data exchanged during model training across multiple devices or parties, thereby improving efficiency and reducing latency. Current research emphasizes techniques like efficient data splitting protocols for decision trees, cross-layer optimization strategies for deep neural networks, and algorithms that balance communication overhead with model accuracy in federated learning, often employing methods such as gradient compression and optimized communication rounds. These advancements are crucial for enabling the training of increasingly large models and facilitating privacy-preserving collaborative learning across diverse network architectures, impacting fields ranging from AI and robotics to wireless communication.