distributed-learning

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.