  Federated learning is a contemporary machine learning paradigm where locally
trained models are distilled into a global model. Due to the intrinsic
permutation invariance of neural networks, Probabilistic Federated Neural
Matching (PFNM) employs a Bayesian nonparametric framework in the generation
process of local neurons, and then creates a linear sum assignment formulation
in each alternative optimization iteration. But according to our theoretical
analysis, the optimization iteration in PFNM omits global information from
existing. In this study, we propose a novel approach that overcomes this flaw
by introducing a Kullback-Leibler divergence penalty at each iteration. The
effectiveness of our approach is demonstrated by experiments on both image
classification and semantic segmentation tasks.
