Incentive Design for Heterogeneous Client Selection: A Robust Federated Learning Approach

Federated learning (FL) allows the collaborative training of machine learning (ML) models between an aggregation server and different clients without sharing their private data. However, the FL archetype is mostly vulnerable to malicious model updates from various clients because of the privacy feature that makes the server see clients as a black-box. When selecting clients, the server has no control on their contributions during training. This opacity of the server towards clients’ data associated with the huge amount of heterogeneous data brings a security risk and poses a deterioration of the model performance in FL. The impact of client selection and data heterogeneity on FL robustness has been overlooked. In this paper, we develop an Incentive Design for Heterogeneous Client Selection (IHCS) to improve the performance while reducing the security risks in FL. The IHCS approach applies a smarter client selection method using cooperative game theory and dynamic clustering of clients based on their heterogeneity level to overcome the challenges of lacking access to clients’ data, unbalanced data, and the lack of applicable data contribution from clients in FL. To do so, we attribute a recognition value to each client using Shapley Value. This recognition index is then used to aggregate the probability of participation level. We also implement, within the IHCS, a heterogeneity-based clustering (HIC) method that helps inhibit the negative influence of data heterogeneity and increase client contributions. Through extensive experiments with empirical results, the proposed approach outperforms the representative works on robustness of FL.