Cooperative Optimization Strategies for Data Collection and Machine Learning in Large-Scale Distributed Systems

With the development of large-scale distributed systems, how to efficiently collect and process data to support machine learning will become an increasingly important problem. However, traditional techniques often treat data acquisition and machine-learning tasks as disjoint processes, resulting in sloppy, less performant solutions. In this paper, we propose a new collaborative optimization framework which simultaneously considers the data acquisition and machine learning tasks. The method has innovatively introduced the feedback mechanism between the data acquisition module and machine learning module based on the current distributed learning model, and has achieved adaptive flexible data selection, intelligent resource allocation and dynamic optimization. Through state-of-the-art approaches like distributed reinforcement learning and data-driven scheduling protocols combined with decentralized gradient descent, the model has demonstrated superior scalability, low latency and precision compared to conventional solutions. It has been illustrated by experimental results that the accuracy of the proposed model is 15% higher than the benchmark method.