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Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate non-sensitive information with their neighbors. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. To handle these issues, we propose a distributed semi-supervised fuzzy regression (DSFR) model with fuzzy if-then rules and interpolation consistency regularization (ICR). The ICR, which was proposed recently for semi-supervised problem, can force decision boundaries to pass through sparse data areas, thus increasing model robustness. However, its application in distributed scenarios has not been considered yet. In this work, we proposed a distributed Fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR. Notably, the DSFR model converges very fast since it does not involve back-propagation procedure and is scalable to large-scale datasets benefiting from the utilization of DFCM and DICR. Experiments results on both artificial and real-world datasets show that the proposed DSFR model can achieve much better performance than the state-of-the-art DSSL algorithm in terms of both loss value and computational cost. Our code is available online.

Item Type:	Refereed Article
Keywords:	distributed semi-supervised learning, fuzzy regression model, fuzzy C-means method, interpolation consistency regularization, alternating direction method of multipliers
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Fuzzy computation
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Artificial intelligence
UTAS Author:	Cao, Z (Dr Zehong Cao)
ID Code:	145509
Year Published:	In Press
Deposited By:	Information and Communication Technology
Deposited On:	2021-07-25
Last Modified:	2021-09-09
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