Federated Learning (FL) addresses the need to create models based on
proprietary data in such a way that multiple clients retain exclusive control
over their data, while all benefit from improved model accuracy due to pooled
resources. Recently proposed Neural Graphical Models (NGMs) are Probabilistic
Graphical models that utilize the expressive power of neural networks to learn
complex non-linear dependencies between the input features. They learn to
capture the underlying data distribution and have efficient algorithms for
inference and sampling. We develop a FL framework which maintains a global NGM
model that learns the averaged information from the local NGM models while
keeping the training data within the client's environment. Our design, FedNGMs,
avoids the pitfalls and shortcomings of neuron matching frameworks like
Federated Matched Averaging that suffers from model parameter explosion. Our
global model size remains constant throughout the process. In the cases where
clients have local variables that are not part of the combined global
distribution, we propose a `Stitching' algorithm, which personalizes the global
NGM models by merging the additional variables using the client's data. FedNGM
is robust to data heterogeneity, large number of participants, and limited
communication bandwidth.

我们开发了一个 FL 框架，其中维护一个全局的 NGM 模型，通过学习本地 NGM 模型的平均信息，同时保持训练数据在客户端环境中。我们的设计 FedNGMs 避免了神经元匹配框架的问题，如 Federated Matched Averaging，在这些框架中存在模型参数膨胀的问题。我们的全局模型大小在整个过程中保持恒定。对于客户端具有不包含在组合全局分布中的本地变量的情况，我们提出了一种 “拼接” 算法，它通过使用客户端数据合并附加变量来个性化全局 NGM 模型。FedNGM 对数据异质性、大量参与者和有限的通信带宽具有鲁棒性。