Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their limited domains, leading to a `domain shift' across clients. Privacy concerns limit each client's learning to its own domain data, which increase the risk of overfitting. Moreover, the process of aggregating models trained on own limited domain can be potentially lead to a significant degradation in the global model performance. To deal with these challenges, we introduce the concept of federated feature diversification. Each client diversifies the own limited domain data by leveraging global feature statistics, i.e., the aggregated average statistics over all participating clients, shared through the global model's parameters. This data diversification helps local models to learn client-invariant representations while preserving privacy. Our resultant global model shows robust performance on unseen test domain data. To enhance performance further, we develop an instance-adaptive inference approach tailored for test domain data. Our proposed instance feature adapter dynamically adjusts feature statistics to align with the test input, thereby reducing the domain gap between the test and training domains. We show that our method achieves state-of-the-art performance on several domain generalization benchmarks within a federated learning setting.

在分布式学习中，我们引入了联邦特征多样化的概念，通过利用全局模型参数共享的聚合平均统计数据，每个本地客户端可以使自身的有限领域数据多样化，以实现学习与客户端无关的表示并保护隐私，并且我们提出了一种针对测试领域数据的实例自适应推理方法，通过动态调整特征统计与测试输入的一致性，从而减小测试和训练领域之间的差距，在联邦学习设置下，在几个领域泛化基准上取得了最先进的性能。

联邦领域泛化的特征多样化与调整