Abstract
Due to stricter data management regulations and large size of the training data, distributed learning paradigm such as federated learning (FL) has gained attention recently. FL is capable of significantly preserving end-users' private data from being exposed to external adversaries. However, private information can still be divulged by uploading parameters from users. Therefore, a key challenge in the FL platform is how users participate to build a high-quality learning model with effectively preventing information leakage. To address the above challenge, we design a novel incentive mechanism to attract more data owners to join in the FL process with the consideration of privacy preservation. To implement our proposed scheme, we adopt the concepts of mechanism design (MD) and differential privacy (DP); MD takes an objectives-first approach to designing incentives toward desired objectives, and the DP can provide a theoretical guarantee for users' privacy in FL participations. Based on the DP based incentive mechanism, our joint approach can leverage the full synergy that gives mutual advantages for users and learning operators. Therefore, we can take various benefits in a rational way under the dynamic changing FL environments. Through simulation analysis, the numerical results validate the performance efficiency of our proposed scheme.
Highlights
With the rapid development of the Internet of Things (IoT) and social network applications, an exponential growth of data has been generated
MAIN CONTRIBUTIONS Based on the concepts of VCG mechanism and differential privacy (DP), we develop a novel federated learning scheme for the future IoT environment
PERFORMANCE EVALUATION we evaluate the performance of our proposed scheme and report the experimental results while comparing with other existing Hierarchical Incentive Federated Learning (HIFL), SIFL and Federated Learning Profit Allocation (FLPA) schemes [1], [7], [11]; these existing schemes are recently published state-of-the-art federated learning (FL) protocols
Summary
With the rapid development of the Internet of Things (IoT) and social network applications, an exponential growth of data has been generated. Based on the reasonably evaluated contribution of each IoT device, the profit earned by the FL model can be allocated to attract more devices to join in the FL process It is a complex and difficult work to design an effective incentive mechanism. According to the VCG mechanism, the learning operator properly induces selfish IoT devices by paying appropriate incentives It can dynamically estimate devices’ contributions, and provides a normative guide for an effective outcome, which is called social optimum. We have adopted the idea of DP to design a new privacy-preserving FL process This approach implies each device’s whole data set, which is protected against differential attacks from others while model performance is kept high in federated learning.
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