  Efficiently aggregating trained neural networks from local clients into a
global model on a server is a widely researched topic in federated learning.
Recently, motivated by diminishing privacy concerns, mitigating potential
attacks, and reducing communication overhead, one-shot federated learning
(i.e., limiting client-server communication into a single round) has gained
popularity among researchers. However, the one-shot aggregation performances
are sensitively affected by the non-identical training data distribution, which
exhibits high statistical heterogeneity in some real-world scenarios. To
address this issue, we propose a novel one-shot aggregation method with
layer-wise posterior aggregation, named FedLPA. FedLPA aggregates local models
to obtain a more accurate global model without requiring extra auxiliary
datasets or exposing any private label information, e.g., label distributions.
To effectively capture the statistics maintained in the biased local datasets
in the practical non-IID scenario, we efficiently infer the posteriors of each
layer in each local model using layer-wise Laplace approximation and aggregate
them to train the global parameters. Extensive experimental results demonstrate
that FedLPA significantly improves learning performance over state-of-the-art
methods across several metrics.
