Collaborative Inference

Collaborative inference aims to optimize deep learning inference by distributing computational tasks across multiple devices, such as edge nodes and cloud servers, to reduce latency, energy consumption, and bandwidth usage while maintaining accuracy. Current research focuses on efficient task partitioning and data sharing strategies, often employing techniques like model parallelism, ensemble methods, and reinforcement learning to optimize resource allocation and communication protocols across heterogeneous networks. This approach holds significant promise for enabling the deployment of complex deep learning models on resource-constrained devices and improving the efficiency and privacy of various applications, including image processing, natural language processing, and sensor data analysis.