  As a promising distributed machine learning paradigm, Federated Learning (FL)
enables all the involved devices to train a global model collaboratively
without exposing their local data privacy. However, for non-IID scenarios, the
classification accuracy of FL models decreases drastically due to the weight
divergence caused by data heterogeneity. Although various FL variants have been
studied to improve model accuracy, most of them still suffer from the problem
of non-negligible communication and computation overhead. In this paper, we
introduce a novel FL approach named Fed-Cat that can achieve high model
accuracy based on our proposed device selection strategy and device
concatenation-based local training method. Unlike conventional FL methods that
aggregate local models trained on individual devices, FedCat periodically
aggregates local models after their traversals through a series of logically
concatenated devices, which can effectively alleviate the model weight
divergence problem. Comprehensive experimental results on four well-known
benchmarks show that our approach can significantly improve the model accuracy
of state-of-the-art FL methods without causing extra communication overhead.
