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. In addition to reviewing state-of-the-art studies, this paper also identifies key challenges and applications in this field, while also highlighting promising future directions.

翻译：联邦学习是一种新型学习范式，通过多方计算与模型聚合实现数据采集与模型训练的解耦。作为灵活的学习框架，联邦学习可与其他学习范式深度融合。本文聚焦联邦学习与其他学习算法的协同研究，系统探讨了改进标准联邦平均算法的多种学习方案，并综述了自适应聚合、正则化、聚类方法与贝叶斯方法等模型融合技术。基于前沿发展趋势，我们进一步探讨了联邦学习与其他学习范式的交叉融合，即联邦X学习（其中X涵盖多任务学习、元学习、迁移学习、无监督学习与强化学习）。除综述最新研究成果外，本文还阐述了该领域的关键挑战与应用场景，并指明了具有前景的未来研究方向。