Research on Data Quality Governance for Federated Cooperation Scenarios

Exploring the data quality problems in the context of federated cooperation and adopting corresponding governance countermeasures can facilitate the smooth progress of federated cooperation and obtain high-performance models. However, previous studies have rarely focused on quality issues in federated cooperation. To this end, this paper analyzes the quality problems in the federated cooperation scenario and innovatively proposes a “Two-stage” data quality governance framework for the federated collaboration scenarios. The first stage is mainly local data quality assessment and optimization, and the evaluation is performed by constructing a metrics scoring formula, and corresponding optimization measures are taken at the same time. In the second stage, the outlier processing mechanism is introduced, and the Data Quality Federated Averaging (Abbreviation DQ-FedAvg) aggregation method for model quality problems is proposed, so as to train high-quality global models and their own excellent local models. Finally, experiments are conducted in real datasets to compare the model performance changes before and after quality governance, and to validate the advantages of the data quality governance framework in a federated learning scenario, so that it can be widely applied to various domains. The governance framework is used to check and govern the quality problems in the federated learning process, and the accuracy of the model is improved.