Approach

Technique

Main Idea

Wang et al. [43]

FedLDA

Develop a novel framework (named FedLDA) for federated learning of LDA models, as well as a novel LDP mechanism called Random Response with Priori (RRP).

Girgis et al. [44]

Shuffle method

Develop shuffle method under with only a random permutation of the clients’ responses are received by the server without their association with the clients’ identities.

Wei et al. [45]

UDP

Develop a novel framework, named UDP, in which each client can achieve adjustable privacy protection levels.

Zhou et al. [46]

PFLF

Develop a novel framework, named PFLF, in which the client and the central server add noise before sending the data.

Thapa et al. [47]

SFL

Develop a novel framework SFL that combines federated learning and split learning to achieve high model accuracy and communication efficiency.

Wu et al. [48]

FedPerGNN

Develop a novel framework FedPerGNN to achieve both effective and privacy-preserving personalization.

Zhang et al. [50]

PriFedSync

Develop federated f-differential privacy and propose a generic framework for private federated learning.