Federated Learning
Federated learning is an advanced machine learning approach that enables models to be trained across multiple decentralized devices or servers holding local data samples, without the need to exchange or centralize these data. This technique is particularly beneficial for privacy preservation, data security, and reducing the need for data transfer, which can be costly and bandwidth-intensive.
In federated learning, a global model is improved iteratively: a shared model is sent to devices, updated locally using their data, and only the model updates (and not the data itself) are sent back to the central server. The server then aggregates these updates to improve the global model. This process leverages the distributed nature of data while maintaining privacy and security, making it ideal for applications where data cannot be shared freely due to confidentiality or regulatory reasons.
One of the most notable applications of federated learning is in the development of predictive text and autocorrect features on smartphones. Each device learns from the individual user's typing habits and preferences without sending sensitive or personal information back to a central server. Instead, only model updates derived from the user's data are sent and aggregated to improve the overall model, which then benefits all users.
Another application is in healthcare, where federated learning allows for the development of predictive models using data from multiple hospitals or clinics without sharing patient data. This can greatly enhance the predictive power of AI tools for diagnosis and treatment recommendations while strictly adhering to patient confidentiality and data privacy regulations.