Cross-silo heterogeneous model federated multitask learning
Document Type
Article
Publication Title
Knowledge-Based Systems
Abstract
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 study, we present a novel FL method CoFED based on unlabeled data pseudolabeling via a process known as cotraining, which meets the needs of heterogeneous models, tasks and training processes in CS-FL. Experimental results suggest that the proposed method outperforms competing methods. This is especially true for non-independent and identically distributed (non-IID) settings and heterogeneous models, where the proposed method achieves a 35% performance improvement.
DOI
10.1016/j.knosys.2023.110347
Publication Date
4-8-2023
Keywords
Cotraining, Federated learning, Heterogeneity, Multitask learning
Recommended Citation
X. Cao, Z. Li, G. Sun, H. Yu, and M. Guizani, "Cross-silo heterogeneous model federated multitask learning", in Knowledge-Based Systems, vol. 265, art. 110347, April 2023, doi:10.1016/j.knosys.2023.110347
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License: CC BY-NC-ND
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