When Federated Learning Meets Other Learning Algorithms: From Model Fusion to Federated X Learning

  • Federated Model Fusion. We categorize the major improvements to the pioneering FedAvg model aggregation algorithm into four subclasses (i.e., adaptive/attentive methods, regularization methods, clustered methods, and Bayesian methods), together with a special focus on fairness.
  • Federated Learning Paradigms. We investigate how the various learning paradigms are fitted into the federated learning setting. The learning paradigms include some key supervised learning scenarios such as transfer learning, multitask and meta-learning, and learning algorithms beyond supervised learning such as semi-supervised learning, unsupervised learning, and reinforcement learning.




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Shaoxiong Ji

Shaoxiong Ji


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