  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.
