  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.
