Federated Aggregation With Interlayer Personalized Contribution: Preference-Based Optimization Between Performance and Privacy

Currently, due to the different distribution of data for each user, many personalized federated learning (PFL) methods have emerged to meet the personalized needs of different users. However, existing methods have two problems: 1) in the aggregation process, the contribution between the internal layers of the client model is not considered and 2) it is difficult to match the quantitative weight information of both user privacy protection and performance with their qualitative preferences during the training process. Therefore, we first propose a framework for federated aggregation with interlayer personalized contribution named FedIPC, which completes model aggregation based on the contribution of internal layers and improves client model performance. Based on the above framework, we design a multiobjective federated optimization method based on adaptive preference indicators named FedAPI-nondominated sorting genetic algorithm II (NSGA-II). This method can match quantitative weight with qualitative user preferences and adaptively select for Pareto optimal solutions during the optimization process. Extensive experiments on two image datasets and a tabular dataset show that our proposed method not only accelerates model convergence, but also achieves good improvements in model performance. In addition, our proposed method can accurately match the qualitative preferences of users, balancing the performance of the model and privacy protection based on preferences.