  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 federated learning
(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
study, we present a novel federated learning method CoFED based on unlabeled
data pseudolabeling via a process known as cotraining. CoFED is a federated
learning method that is compatible with heterogeneous models, tasks, and
training processes. The experimental results suggest that the proposed method
outperforms competing ones. This is especially true for non-independent and
identically distributed settings and heterogeneous models, where the proposed
method achieves a 35% performance improvement.
