  In this paper, we investigate the problem of decentralized federated learning
(DFL) in Internet of things (IoT) systems, where a number of IoT clients train
models collectively for a common task without sharing their private training
data in the absence of a central server. Most of the existing DFL schemes are
composed of two alternating steps, i.e., gradient update and model averaging.
However, averaging of model parameters directly to fuse different models at the
local clients suffers from client-drift in the local updates especially when
the training data are heterogeneous across different clients. This leads to
slow convergence and degraded learning performance. As a possible solution, we
propose the decentralized federated learning via mutual knowledge transfer
(Def-KT) algorithm where local clients fuse models by transferring their learnt
knowledge to each other. Our experiments on the MNIST, Fashion-MNIST, and
CIFAR10 datasets reveal that the proposed Def-KT algorithm significantly
outperforms the baseline DFL methods with model averaging, i.e., Combo and
FullAvg, especially when the training data are not independent and identically
distributed (non-IID) across different clients.
