Cross-Silo Heterogeneous Model Federated Multitask Learning

Federated learning (FL) is a machine learning technique that enables participants to collaboratively train high-quality models without exchanging their private data. Participants utilizing cross-silo FL (CS-FL) settings are independent organizations with different task needs, and they are concerned not only with data privacy but also with independently training their unique models due to intellectual property considerations. Most existing FL methods are incapable of satisfying the above scenarios. In this paper, we propose a FL method based on the pseudolabeling of unlabeled data via a process such as cotraining. To the best of our knowledge, this is the first FL method that is simultaneously compatible with heterogeneous tasks, heterogeneous models, and heterogeneous training algorithms. Experimental results show that the proposed method achieves better performance than competing ones. This is especially true for non-independent and identically distributed (IID) settings and heterogeneous models, where the proposed method achieves a 35% performance improvement.