Secure aggregation in federated learning is a technique designed to protect the privacy of individual participants while still allowing their contributions to improve a shared model. In federated learning, multiple devices or clients train a machine learning model collaboratively without sharing their raw data with each other or a central server. Secure aggregation ensures that the server can compute the aggregated updates from the clients without being able to see the individual updates themselves, thereby preserving the confidentiality of the data.
The process usually involves encrypting the model updates before they are sent from each client to the server. For example, each client generates an update based on its local data and, instead of sending this update directly to the server, it might first encrypt it using a secure method. The server collects these encrypted updates and combines them in a way that allows it to compute an aggregate update while remaining oblivious to the content of individual updates. This can be achieved through techniques like homomorphic encryption or secure multi-party computation, where mathematical operations are performed on encrypted data without decrypting it.
By implementing secure aggregation, developers can contribute to enhancing the privacy and security of federated learning systems. For instance, consider a scenario where users' smartphone models are trained to improve predictive text while keeping their typing habits private. With secure aggregation, even though the server is receiving model updates based on users' typing data, it cannot access or infer any personal information about individual users. This increases user trust and makes it more acceptable for them to participate in federated learning initiatives, ultimately leading to better model performance without compromising privacy.