How is data encrypted in federated learning?

In federated learning, data encryption is handled indirectly by securing model updates rather than raw data. Since the core principle of federated learning is to keep data decentralized, raw user data never leaves the local device. Instead, encryption techniques focus on protecting the model parameters or gradients shared between clients and the central server. Three common approaches are secure aggregation, homomorphic encryption, and differential privacy. These methods ensure that sensitive information isn’t exposed during the training process, even if updates are intercepted or analyzed.

Secure aggregation is a widely used protocol that encrypts model updates from multiple clients before they’re combined. For example, Google’s federated learning system for mobile keyboard predictions uses this method. Clients encrypt their local model gradients with cryptographic keys, and the server can only decrypt the aggregated result of all updates, not individual contributions. Homomorphic encryption takes this further by allowing computations on encrypted data—clients send encrypted model updates, and the server performs mathematical operations (like averaging) without decrypting them. A healthcare application might use this to ensure patient data remains private while training a shared model across hospitals. Differential privacy adds noise to model updates to prevent reverse-engineering of raw data. Apple employs this in features like Siri suggestions, where random noise is injected into updates to obscure individual user patterns.

Each method has trade-offs. Secure aggregation requires coordination between clients to manage encryption keys, which adds complexity. Homomorphic encryption is computationally expensive, making it impractical for large models. Differential privacy can reduce model accuracy if too much noise is added. Developers often combine these techniques—for example, using secure aggregation with lightweight differential privacy—to balance privacy and efficiency. Libraries like TensorFlow Federated and PySyft provide tools to implement these methods, abstracting cryptographic details. The key takeaway is that encryption in federated learning isn’t about encrypting the data itself but ensuring that the shared information (model updates) doesn’t leak sensitive details during collaboration.