Distributed machine learning refers to the training of artificial intelligence models across multiple computational nodes or devices. This methodology segments data or model components among various participants, facilitating parallel computation and cooperative learning. It seeks to augment scalability, improve privacy, and leverage geographically dispersed resources for model development.
Context
The application of distributed machine learning is gaining relevance within decentralized AI projects and privacy-preserving data analysis on blockchain platforms. This approach allows for the joint advancement of models without consolidating sensitive data, which is essential for regulatory adherence and user confidence in digital asset services. Ongoing efforts focus on refining communication efficiency and preserving model fidelity across distributed environments.
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