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An Explainable Semi-Personalized Federated Learning Model
Demertzis, Konstantinos et al. ‘An Explainable Semi-personalized Federated Learning Model’. 1 Jan. 2022 : 335 – 350. This paper proposes an innovative hybrid explainable semi-personalized federated learning model, that utilizes Shapley Values and Lipschitz Constant techniques, in order to create personalized intelligent models. It is based on the needs and events that each individual user is required to address in a federated format. Explanations are the assortment of characteristics of the interpretable system, which, in the case of a specified illustration, helped to bring about a conclusion and provided the function of the model on both local and global levels. Retraining is suggested only for those features for which the degree of change is considered quite important for the evolution of its functionality.