  This paper addresses the problem of decentralized learning to achieve a
high-performance global model by asking a group of clients to share local
models pre-trained with their own data resources. We are particularly
interested in a specific case where both the client model architectures and
data distributions are diverse, which makes it nontrivial to adopt conventional
approaches such as Federated Learning and network co-distillation. To this end,
we propose a new decentralized learning method called Decentralized Learning
via Adaptive Distillation (DLAD). Given a collection of client models and a
large number of unlabeled distillation samples, the proposed DLAD 1) aggregates
the outputs of the client models while adaptively emphasizing those with higher
confidence in given distillation samples and 2) trains the global model to
imitate the aggregated outputs. Our extensive experimental evaluation on
multiple public datasets (MNIST, CIFAR-10, and CINIC-10) demonstrates the
effectiveness of the proposed method.
