Cross-Silo Heterogeneous Model Federated Multitask Learning
This addresses privacy and intellectual property concerns for organizations in federated learning, though it appears incremental as it builds on existing FL methods with a novel adaptation.
The paper tackles the problem of cross-silo federated learning where organizations need to train unique models without sharing data or intellectual property, and it introduces CoFED, a method based on cotraining that achieves a 35% performance improvement in non-IID and heterogeneous model settings.
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.