Federated learning is a decentralized approach to machine learning that allows multiple devices or data sources to collaborate on model training without sharing their local data. The primary privacy-preserving techniques used in federated learning are model aggregation, differential privacy, and secure multiparty computation. Each of these techniques helps protect sensitive user data while still enabling the system to learn from it.
Model aggregation involves collecting model updates from multiple participants rather than their raw data. After local models are trained on individual devices, only the model parameters or gradients are sent to a central server. The server then averages these updates to create a global model. This method ensures that individual data remains on local devices, significantly reducing the likelihood of data exposure. However, it's crucial to implement mechanisms that prevent participants from sharing their updates in a way that could inadvertently reveal information about their local datasets.
Differential privacy adds an extra layer of security by introducing noise into the model updates before they are sent to the central server. This noise prevents any single update from revealing too much information about an individual's data. For instance, if a participant's update could potentially reveal sensitive information, applying differential privacy ensures that the impact of that update is masked. By controlling the amount of noise added, developers can strike a balance between data privacy and model accuracy, allowing for a more robust and secure federated learning process.