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：联邦学习是一种新型学习范式，通过多方计算和模型聚合实现数据收集与模型训练的解耦。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。本文针对联邦学习与其他学习算法的结合开展聚焦性综述研究。具体而言，我们探索了用于改进原始联邦平均算法的各类学习算法，并梳理了自适应聚合、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，本文进一步探讨了联邦学习与其他学习范式的交叉领域，即联邦X学习（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）。本综述系统评述了该领域的研究现状、关键挑战与未来发展方向。