Federated transfer learning (FTL) is a machine learning approach that combines two concepts: federated learning and transfer learning. Federated learning enables multiple parties to collaboratively train a model without sharing their raw data, preserving privacy. Transfer learning allows a model developed for one task or dataset to be reused or adapted for a different but related task. In FTL, these ideas merge to let organizations or devices leverage knowledge from a source domain (e.g., one company’s data) to improve performance on a target domain (e.g., another company’s task) without exchanging sensitive data. For example, a hospital could use FTL to adapt a model trained on another hospital’s patient data to its own local population, even if legal restrictions prevent direct data sharing.
FTL works by structuring how knowledge is transferred across participants. Typically, a base model is trained on the source party’s data. This model’s parameters or features are then shared with the target party, which fine-tunes the model using its own data. The process is coordinated through a central server or peer-to-peer communication, ensuring raw data stays decentralized. For instance, a retail chain might train a base recommendation model on data from regions with abundant customer interactions, then adapt it for regions with sparse data by combining local updates with the shared model. Techniques like feature extraction (reusing learned patterns) or parameter freezing (locking parts of the model during fine-tuning) are common. This approach reduces the need for large local datasets while maintaining privacy.
The main benefits of FTL include privacy preservation, reduced data collection costs, and the ability to collaborate across domains with differing data distributions. However, challenges include communication overhead, aligning heterogeneous data structures, and ensuring robustness against malicious participants. For example, financial institutions might use FTL to detect fraud by transferring patterns from a bank with extensive fraud data to another with limited examples, all while keeping transaction details private. To address security concerns, methods like differential privacy or homomorphic encryption can be applied during model updates. While FTL requires careful coordination, it offers a practical path for organizations to share insights without compromising sensitive information.