Description: Synchronous learning is an approach within federated learning that allows multiple clients to update their models simultaneously during each training round. This method is characterized by its ability to coordinate the learning of multiple devices or nodes, ensuring that all participate in the training process at the same time. This contrasts with asynchronous learning, where models are updated independently and at different times. Synchronization in synchronous learning is crucial for maintaining the consistency and integrity of the models, as it allows all participants to contribute their data and experiences within a common time frame. This approach is especially relevant in environments where data privacy and security are paramount, as it enables devices to train models without needing to share sensitive data. Additionally, synchronous learning can enhance the efficiency of the training process, as it allows for faster convergence of the models by leveraging information from all clients simultaneously. In summary, synchronous learning is a powerful technique that combines collaboration from multiple data sources with the need to maintain privacy and security, making it a valuable tool in the field of machine learning and artificial intelligence.
