Federated learning and centralized learning represent two distinct approaches to training machine learning models. In centralized learning, data is gathered from various sources and stored in a single location, where a model is trained using this collective dataset. For instance, a company may collect user data from its mobile app and train a recommendation system on servers. This approach allows for a comprehensive view of the data, enabling the model to potentially achieve higher accuracy due to the vast amount of information it can leverage.
On the other hand, federated learning takes a different stance by keeping the data on local devices rather than centralizing it. In this approach, the model is shared with multiple devices, which independently train the model on their local data. Only the model updates (like gradients) are sent back to a central server for aggregation. For example, consider a smartphone app that improves its predictive text feature by learning from the text users type without sending all the text data to a central server. This method prioritizes user privacy, as sensitive data remains on the user's device, reducing the risk of data breaches.
Furthermore, federated learning is particularly beneficial in scenarios where data is large and decentralized, or regulations limit data sharing. It enables collaboration among multiple entities while respecting data ownership and privacy laws. In centralized learning, all data is controlled by one organization, which can create issues if that entity faces regulatory scrutiny. In contrast, federated learning allows organizations to build robust models while maintaining compliance and fostering trust with users, thus addressing privacy concerns more effectively.