  Federated learning (FL) has emerged as a secure paradigm for collaborative
training among clients. Without data centralization, FL allows clients to share
local information in a privacy-preserving manner. This approach has gained
considerable attention, promoting numerous surveys to summarize the related
works. However, the majority of these surveys concentrate on FL methods that
share model parameters during the training process, while overlooking the
possibility of sharing local information in other forms. In this paper, we
present a systematic survey from a new perspective of what to share in FL, with
an emphasis on the model utility, privacy leakage, and communication
efficiency. First, we present a new taxonomy of FL methods in terms of three
sharing methods, which respectively share model, synthetic data, and knowledge.
Second, we analyze the vulnerability of different sharing methods to privacy
attacks and review the defense mechanisms. Third, we conduct extensive
experiments to compare the learning performance and communication overhead of
various sharing methods in FL. Besides, we assess the potential privacy leakage
through model inversion and membership inference attacks, while comparing the
effectiveness of various defense approaches. Finally, we identify future
research directions and conclude the survey.
