Personalization in federated learning involves tailoring machine learning models to individual users while keeping their data decentralized. In this approach, instead of sending users' data to a central server for training, federated learning allows individual devices to train a shared model locally. The device then sends only the updated model parameters back to the central server, which aggregates these updates to improve the global model. The key to personalization lies in adapting the global model to account for unique characteristics and preferences of individual users.
One common way to achieve personalization is through a technique called fine-tuning. After the central server trains the initial global model using data from multiple devices, users' devices can perform additional training with their local data. For instance, consider a keyboard app that uses federated learning to predict user text inputs. The global model captures general typing patterns, but each user's typing style can differ significantly. By fine-tuning the global model with individual typing data, the app becomes more responsive to a user's specific vocabulary and typing habits, resulting in a better experience.
In addition to fine-tuning, personalization can also be implemented by using user-specific model components. This means that while the central server maintains a shared model, each user has their own unique layer or parameters that adjust the global model based on personal data. For example, in a personalized recommendation system for an e-commerce platform, the central model might suggest popular items, but each user could have specific recommendations based on their past purchases or browsing history. By combining general insights with individual preferences, federated learning enables a more tailored approach without compromising user privacy.