A survey on federated learning in crowd intelligence

Abstract

Crowd intelligence is emerging as a new artificial intelligence paradigm owing to the rapid development of the Internet.However, the data isolation and data privacy preservation problems make it difficult to share data among the crowd and to build crowd intelligent applications.Federated learning is a novel solution that aims to collaboratively build models by breaking the data barriers in crowd.Firstly, the basic ideas of federated learning and a comparison with crowd intelligence were introduced.Secondly, federated learning algorithms were divided into three categories according to the crowd organization, and further optimization techniques on privacy, accuracy and efficiency were discussed.Thirdly, federated learning operators based on linear models, tree models and neural network models were presented respectively.Finally, mainstream federated learningopensource platforms and typical applications were introduced, followed by the conclusion.

Keywords

• crowd intelligence;federated learning;privacy preservation