Federated Learning (FL) is a privacy-preserving machine learning framework facilitating collaborative training across distributed clients. However, its performance is often compromised by data heterogeneity among participants, which can result in local models with limited generalization capability. Traditional model-homogeneous approaches address this issue primarily by regularizing local training procedures or dynamically adjusting client weights during aggregation. Nevertheless, these methods become unsuitable in scenarios involving clients with heterogeneous model architectures. In this paper, we propose a model-heterogeneous FL framework that enhances clients' generalization performance on unseen data without relying on parameter aggregation. Instead of model parameters, clients share feature distribution statistics (mean and covariance) with the server. Then each client trains a variational transposed convolutional neural network using Gaussian latent variables sampled from these distributions, and use it to generate synthetic data. By fine-tuning local models with the synthetic data, clients achieve significant improvement of generalization ability. Experimental results demonstrate that our approach not only attains higher generalization accuracy compared to existing model-heterogeneous FL frameworks, but also reduces communication costs and memory consumption.

翻译：联邦学习是一种保护隐私的机器学习框架，旨在促进分布式客户端间的协同训练。然而，其性能常因参与者间的数据异质性而受限，这可能导致局部模型泛化能力不足。传统的模型同构方法主要通过正则化局部训练过程或在聚合时动态调整客户端权重来解决此问题。然而，这些方法在涉及异构模型架构客户端的场景中变得不再适用。本文提出一种模型异构联邦学习框架，该框架在不依赖参数聚合的情况下，提升客户端对未见数据的泛化性能。客户端不与服务器共享模型参数，而是共享特征分布统计量（均值与协方差）。随后，每个客户端使用从这些分布中采样的高斯潜变量训练一个变分转置卷积神经网络，并利用其生成合成数据。通过使用合成数据对局部模型进行微调，客户端的泛化能力得到显著提升。实验结果表明，与现有模型异构联邦学习框架相比，我们的方法不仅获得了更高的泛化准确率，同时降低了通信开销与内存消耗。