Federated learning differs from centralized learning primarily in how data is stored, processed, and communicated during model training. In centralized learning, all training data is collected and stored on a single server or data center. The model trains directly on this centralized dataset, which requires transferring raw data from individual devices or sources to the server. In contrast, federated learning keeps data decentralized: training occurs locally on devices (e.g., smartphones, IoT sensors) or edge servers, and only model updates (like gradients or weights) are sent to a central coordinator. This approach avoids moving raw data, addressing privacy and bandwidth constraints.
For example, consider a healthcare application. In centralized learning, patient records from multiple hospitals would need to be aggregated into one location, raising privacy and regulatory concerns. With federated learning, each hospital trains a model on its local data and shares only the model’s learned parameters. The central server aggregates these updates to create a global model without ever accessing raw patient data. This decentralized process reduces exposure to data breaches and complies with regulations like GDPR or HIPAA.
However, federated learning introduces challenges that centralized approaches avoid. Communication overhead increases because the central server must synchronize updates across many devices, which may have unreliable connectivity. Data heterogeneity—where local datasets differ significantly in distribution (e.g., user typing habits on smartphones)—can lead to model bias if not handled carefully. Centralized learning avoids these issues by training on uniformly accessible data, simplifying debugging and optimization. For instance, training a recommendation system centrally allows developers to inspect the entire dataset for imbalances, whereas federated learning requires techniques like federated averaging or differential privacy to mitigate uneven data quality.
In summary, federated learning prioritizes data privacy and reduces bandwidth usage by keeping data on-device, while centralized learning offers simplicity and direct control over training data. Developers must choose based on their use case: federated is ideal for sensitive or geographically distributed data (e.g., smart keyboards improving without sharing user text), while centralized suits scenarios where data aggregation is feasible and privacy risks are manageable (e.g., internal enterprise analytics).