distributed-learning
Expanded definition
Distributed learning allows for training models on large datasets that cannot fit on a single machine. This approach can significantly reduce the time required for training by leveraging the computational power of multiple devices working in parallel. A common misconception is that it always results in better performance; however, it often depends on the communication efficiency and data synchronization between nodes.
Related terms
Explore adjacent ideas in the knowledge graph.
distributed-learning FAQ
What is distributed-learning?
A machine learning paradigm where the training data is distributed across multiple devices or nodes.
How is distributed-learning used in AI systems?
Distributed learning allows for training models on large datasets that cannot fit on a single machine. This approach can significantly reduce the time required for training by leveraging the computational power of multiple devices working in parallel.
Related
Comparisons, tools, and models that connect to this idea.