What are the key components of a federated learning system?

A federated learning system enables machine learning models to be trained across decentralized devices or servers without sharing raw data. The key components include a central coordinator, client devices, communication protocols, aggregation algorithms, and mechanisms for privacy and security. These components work together to ensure models learn from distributed data while maintaining data locality and compliance with privacy constraints.

The first critical component is the central coordinator (often a server), which orchestrates the training process. This server initializes the global model, distributes it to clients, and aggregates updates received from them. For example, in a mobile keyboard app, the server might send a language model to user devices. Each device trains the model locally using the user’s typing data, then sends only the model updates (e.g., gradient values) back to the server. The server averages these updates to improve the global model. The coordinator must handle varying participation rates, device failures, and network delays, often using strategies like federated averaging (FedAvg) to combine updates efficiently.

The second component is the client-side infrastructure, which includes the devices or servers that perform local training. Clients must have sufficient computational resources to run training iterations and storage to cache data. For instance, in a healthcare federated learning system, hospitals might act as clients, training a diagnostic model on local patient records without sharing sensitive data. Client software typically includes a training loop that executes optimization steps (e.g., stochastic gradient descent) and communicates updates securely. To minimize resource usage, techniques like quantization (reducing update precision) or selective parameter updates (sending only critical changes) are often applied. Clients also need mechanisms to handle intermittent connectivity, such as caching updates until a connection is restored.

The third component is the communication and security layer, which ensures reliable and secure data exchange. Communication protocols must balance efficiency and reliability, especially when dealing with thousands of devices. For example, HTTP/2 or gRPC might be used for efficient bidirectional messaging. Security measures like encryption (e.g., TLS for transit) and secure aggregation protocols (e.g., using homomorphic encryption or multiparty computation) prevent adversaries from reconstructing raw data from model updates. Additionally, differential privacy techniques can add noise to updates to further obscure individual contributions. These layers ensure that even if a malicious actor intercepts updates, they cannot reverse-engineer sensitive information from the training process.