In federated learning, the server acts as a central coordinator to manage the collaborative training of a machine learning model across decentralized devices or clients. Its primary role is to orchestrate the training process by distributing the initial model, aggregating updates from clients, and ensuring the system operates efficiently while preserving data privacy. Unlike traditional centralized training, the server does not directly access raw data from clients—instead, it handles model parameters or gradients, enabling training without exposing sensitive information.
The server first initializes a global model and shares it with participating clients, such as smartphones, IoT devices, or edge servers. For example, a healthcare application might involve hospitals training a model on patient data stored locally at each institution. The server then waits for clients to compute updates using their local data. These updates—often gradients or adjusted model weights—are sent back to the server, which aggregates them to improve the global model. A key task here is handling inconsistencies, such as clients dropping out or submitting updates at different times, while maintaining training stability.
Finally, the server employs aggregation algorithms (e.g., Federated Averaging) to combine client updates into a single improved global model. This involves weighting contributions based on factors like dataset size or data quality. For instance, a server might prioritize updates from devices with larger, more representative datasets. It also enforces security measures, such as encryption or secure multi-party computation, to prevent malicious actors from tampering with updates. By iterating this process—distribute, collect, aggregate—the server progressively refines the model until it achieves satisfactory performance, all while keeping client data decentralized and private.