Federated multitask learning (FMTL) and standard federated learning (FL) are both approaches designed to enable learning from distributed data without centralizing it. The key difference lies in their objectives and how they utilize the data at the client devices. In standard federated learning, the focus is on training a single global model based on the data distributed across multiple clients. Each client trains the model locally using its data and then sends the model updates back to a central server, which aggregates these updates to improve the global model. The primary objective is often to enhance the model’s performance on a singular task across various environments.
On the other hand, federated multitask learning allows for training multiple models simultaneously with different tasks on the client devices. In this setup, each client can have its unique data distribution and its own tasks, but the system learns to leverage the shared knowledge among these tasks. For example, in a healthcare setting, one hospital might have data related to diabetes prediction while another has data for heart disease prediction. In federated multitask learning, the system would learn models capable of addressing both tasks, benefiting from the shared learning experience across different hospitals without compromising patient privacy.
This difference in approach brings unique advantages. In federated multitask learning, knowledge transfer occurs between related tasks, improving the overall performance and generalizability of the models. By utilizing data from multiple sources and tasks, the models can learn better representations, thus enhancing their effectiveness. In standard federated learning, the lack of task diversity can limit the model’s ability to generalize across unseen data or tasks, while FMTL actively seeks to bridge the knowledge gap between differing tasks, resulting in a richer learning experience.