Traditional Federated Learning （FL） does not consider collaborative fairness， leading to a mismatch between the reward obtained by the client and its actual contribution. To address this issue， a Federated learning fairness algorithm based on Personalized Submodel and K-means clustering （FedPSK） was proposed. Firstly， the neurons in the neural network were clustered according to their activation patterns， and only the importance of the cluster center neurons after clustering was evaluated. And the scores of the cluster center neurons were used to represent the scores of other neurons in the cluster， which reduced the time consumption of neuron evaluation. Then， the number of neurons and their labeling included in the client submodel were selected through hierarchical selection method， and a submodel with a complete neural network structure was constructed for each client. Finally， collaborative fairness was achieved by distributing submodels to the clients. Experimental results on different datasets show that FedPSK improves the correlation coefficient of fairness measurement by 2.70% compared with FedSAC （Federated learning framework with dynamic Submodel Allocation for Collaborative fairness）. In terms of time overhead， FedPSK reduces by at least 84.12% compared with FedSAC. It can be seen that FedPSK improves the fairness of FL algorithm， and reduces the time overhead of the algorithm execution greatly， verifying the efficiency of the proposed algorithm.
