论文标题
客户适应可以通过模拟的非IID客户来改善联合学习
Client Adaptation improves Federated Learning with Simulated Non-IID Clients
论文作者
论文摘要
我们提出了一种联合学习方法，用于学习客户的适应性，可靠的模型，当时数据是非相同和非独立的分布式（非IID）的。通过模拟异构客户，我们表明添加学习的客户条件可改善模型性能，并且该方法显示在音频和图像域中都可以在平衡且不平衡的数据集上工作。客户适应由有条件的封闭激活单元实现，当每个客户的数据分布之间存在很大差异时，这是联合学习中的常见情况。
We present a federated learning approach for learning a client adaptable, robust model when data is non-identically and non-independently distributed (non-IID) across clients. By simulating heterogeneous clients, we show that adding learned client-specific conditioning improves model performance, and the approach is shown to work on balanced and imbalanced data set from both audio and image domains. The client adaptation is implemented by a conditional gated activation unit and is particularly beneficial when there are large differences between the data distribution for each client, a common scenario in federated learning.
