Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks. We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed federated X learning, where X includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. This survey reviews the state of the art, challenges, and future directions.

翻译：联邦学习是一种新的学习范式，通过多方计算和模型聚合解耦数据收集与模型训练。作为一种灵活的学习框架，联邦学习具有与其他学习框架集成的潜力。我们针对联邦学习与其他学习算法的结合进行了重点综述。具体而言，我们探索了多种改进朴素联邦平均算法的学习算法，并综述了自适应聚合、正则化、聚类方法和贝叶斯方法等模型融合技术。顺应新兴趋势，我们还讨论了联邦学习与其他学习范式交叉的领域——即联邦X学习，其中X包括多任务学习、元学习、迁移学习、无监督学习和强化学习。本综述回顾了相关前沿技术、挑战及未来发展方向。