How does federated learning comply with data privacy regulations like GDPR?

Federated learning (FL) aligns with data privacy regulations like the GDPR by design, primarily because it avoids centralizing raw user data. In FL, machine learning models are trained locally on user devices (e.g., smartphones or edge servers), and only model updates—not raw data—are sent to a central server for aggregation. This approach inherently reduces the risk of exposing personal data, a core requirement of GDPR. For example, a keyboard app using FL could improve autocorrect suggestions by training on local typing data without ever transmitting sensitive text to a central database. By keeping data on-device, FL minimizes the scope of data processing, which directly supports GDPR principles like data minimization (Article 5(1)(b)) and storage limitation (Article 5(1)(e)).

GDPR emphasizes user control over data, including the right to access, correct, or delete personal information (Articles 15–17). FL simplifies compliance with these rights because data remains on the user’s device. For instance, if a user requests deletion of their data under Article 17, the local dataset can be erased directly from their device, ensuring the central model no longer reflects their information in future updates. However, challenges arise when past model updates might still contain traces of a user’s data. To address this, some FL systems use techniques like federated unlearning, which retroactively removes a user’s influence from the aggregated model. Additionally, FL frameworks can log user participation to streamline compliance audits, ensuring accountability (Article 5(2)).

Technical safeguards in FL further strengthen GDPR compliance. For example, secure aggregation protocols encrypt model updates during transmission, preventing servers from linking updates to individual users. Differential privacy can also be applied by adding statistical noise to updates, making it harder to infer raw data from the model. In healthcare applications, where data sensitivity is high, FL might combine homomorphic encryption to compute aggregated model changes without decrypting individual contributions. These measures align with GDPR’s requirement for “data protection by design” (Article 25). However, developers must still ensure proper user consent mechanisms (e.g., explaining how local data is used for training) and validate that third-party libraries or hardware used in FL workflows don’t inadvertently leak data. Regular audits and transparency reports can help maintain trust and compliance.