论文标题
对联邦学习的个性化技术调查
Survey of Personalization Techniques for Federated Learning
论文作者
论文摘要
联合学习使机器学习模型能够从私人分散数据中学习，而不会损害隐私。联合学习的标准配方为所有客户提供了一个共享模型。由于跨设备数据的非IID分布而引起的统计异质性通常会导致场景，对于某些客户而言，仅根据其私人数据培训的本地模型的表现就比全球共享模型更好，从而消除了他们参与该过程的动力。已经提出了几种技术来个性化全球模型，以便为个别客户更好地工作。本文凸显了对个性化的需求，并调查了有关该主题的最新研究。
Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients. Statistical heterogeneity due to non-IID distribution of data across devices often leads to scenarios where, for some clients, the local models trained solely on their private data perform better than the global shared model thus taking away their incentive to participate in the process. Several techniques have been proposed to personalize global models to work better for individual clients. This paper highlights the need for personalization and surveys recent research on this topic.
