👉 Dealer computing is a distributed computing approach used in machine learning, particularly for training large models, where the computation and data are split across multiple devices or nodes, rather than relying on a single powerful server. In dealer computing, the dataset is partitioned into smaller chunks and distributed among various clients or nodes, which perform computations on their local data. These clients communicate with a central coordinator, often referred to as the dealer, which aggregates the results from all nodes to update the global model. This method allows for efficient training on massive datasets that would otherwise be too large to fit into the memory of a single machine, while also providing fault tolerance and scalability. By leveraging the collective computational power of multiple devices, dealer computing enables the training of complex models in a more practical and cost-effective manner.
