Research On Intrinsic Incentive Mechanism Of Federated Learning Based On Game Theory

Federated Learning(FL)is an innovative distributed machine learning paradigm designed to address data silos and privacy protection issues in large-scale machine learning.Federated learning enables data privacy protection and collaborative model training by sharing model updates rather than raw data among multiple participants.However,in real-world applications,many users refuse to participate in federated training tasks due to computational overhead and communication costs.Therefore,existing federated learning systems typically establish an incentive mechanism framework based on contribution assessment and reward distribution to encourage user participation.Unfortunately,users attracted by external incentives often exhibit a lack of enthusiasm for training or report false information,which severely impacts model training accuracy.Research indicates that relying solely on external incentives is insufficient for generating sustainable and effective user stimulation,often requiring high costs to maximize incentive effects.To address these challenges,this study analyzes and summarizes recent research literature in the field of federated learning incentive mechanisms,proposing a federated learning incentive mechanism based on ego drive aimed at motivating participants’ intrinsic motivations.This involves introducing intrinsic incentives into traditional incentive mechanisms to compensate for their shortcomings.This paper innovates on the design and utility evaluation methods of intrinsic incentive mechanisms in federated learning,with the following key research findings:(1)Propose an incentive mechanism for federated learning based on ego drive.By summarizing and analyzing relevant literature in recent years,the study presents the concept of designing incentive mechanisms based on ego drive.The study clarifies the utility assessment function for intrinsic incentives in federated learning incentive mechanisms and abstracts various intrinsic motivating factors into users’ internal drive to enhance their control over the surrounding environment.This provides a theoretical foundation for the research and implementation of incentive mechanisms in federated learning.(2)A group collaboration competition mechanism based on the K-Means++ algorithm is designed.This mechanism enhances system uncertainty and achieves federated utility maximization through fair grouping and task completion assessment.(3)A task pre-settlement mechanism based on information transparency is designed.By incorporating a gamification perspective of training tasks and task completion predictions,this mechanism combines scoring and reward-penalty modules to improve system information transparency and reduce user reward delay losses while ensuring privacy protection.(4)The interaction between the server and the client in the federated learning system is modeled as a multi-role,multi-stage Stackelberg game process involving model providers,model trainers,and model users.The utility of ego drive is parameterized and incorporated into the utility functions of each role.By employing an optimized game framework in the federated learning system,the maximization of federated utility is achieved.Finally,a system characteristic analysis and experimental validation are conducted,verifying the effectiveness of the proposed mechanism from both theoretical and practical perspectives.