Name
Abstract | ||
|---|---|---|
Federated Learning is a framework that coordinates a large amount of workers to train a shared model in a distributed manner, in which the training data are located on the workers' sides in order to preserve data privacy. There are two challenges in the crowdsourcing of FL, the workers who participant in training need to consume computing and communication resources, so that they are reluctant to participate in the training process if they can not get reasonable rewards. Moreover, there may be attackers who send arbitrary updates to get undeserving compensation or even destroy the model, thus, effective prevention of malicious workers is also critical. An incentive mechanism is urgently required in order to encourage high-quality workers to participate in FL and to punish the attackers. In this paper, we propose FGFL, a blockchain-based incentive governor for Federated Learning. In FGFL, we assess the participants with reputation and contribution indicators. Then the task publisher rewards workers fairly to attract efficient ones while the malicious ones are punished and eliminated. In addition, we propose a blockchain-based incentive management system to manage the incentive mechanism. We evaluate the effectiveness and fairness of FGFL through theoretical analysis and comprehensive experiments. The evaluation results show that FGFL fairly rewards workers according to their corresponding behavior and quality. FGFL increases the system revenue by 0.2% to 3.4% in reliable federations compared with baselines. And in the unreliable scenario where contains attackers, the system revenue of FGFL outperforms the baselines by more than 46.7%. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.jpdc.2022.01.019 | Journal of Parallel and Distributed Computing |
Keywords | DocType | Volume |
Federated Learning,Incentive mechanism,Attack detection | Journal | 163 |
ISSN | Citations | PageRank |
0743-7315 | 0 | 0.34 |
References | Authors | |
33 | 5 | |