  Federated learning (FL) is a novel learning paradigm that addresses the
privacy leakage challenge of centralized learning. However, in FL, users with
non-independent and identically distributed (non-IID) characteristics can
deteriorate the performance of the global model. Specifically, the global model
suffers from the weight divergence challenge owing to non-IID data. To address
the aforementioned challenge, we propose a novel diffusion strategy of the
machine learning (ML) model (FedDif) to maximize the FL performance with
non-IID data. In FedDif, users spread local models to neighboring users over
D2D communications. FedDif enables the local model to experience different
distributions before parameter aggregation. Furthermore, we theoretically
demonstrate that FedDif can circumvent the weight divergence challenge. On the
theoretical basis, we propose the communication-efficient diffusion strategy of
the ML model, which can determine the trade-off between the learning
performance and communication cost based on auction theory. The performance
evaluation results show that FedDif improves the test accuracy of the global
model by 10.37% compared to the baseline FL with non-IID settings. Moreover,
FedDif improves the number of consumed sub-frames by 1.28 to 2.85 folds to the
latest methods except for the model compression scheme. FedDif also improves
the number of transmitted models by 1.43 to 2.67 folds to the latest methods.
