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| Title | Research On Federated Learning'S Contribution To Trustworthy And Responsible Artificial Intelligence |
|---|---|
| ID_Doc | 45392 |
| Authors | Huang S.; Liang Y.; Shen F.; Gao F. |
| Year | 2024 |
| Published | ACM International Conference Proceeding Series |
| DOI | http://dx.doi.org/10.1145/3689299.3689322 |
| Abstract | As an emerging machine learning paradigm, federated learning allows multiple partners to work together to complete specific machine learning tasks while ensuring that the original data of participants is not leaked. This method effectively solves the problem of”data islands” in traditional centralized machine learning, and has been widely used in many fields such as smart healthcare, smart cities, and finance. Federated learning (FL), as a key technology to drive the development of trustworthy and responsible AI, provides a mechanism for sharing learning experiences without directly exchanging data. Federated learning, as a key technology to drive the development of trustworthy and responsible AI, provides a mechanism to share learning experiences without the need to directly exchange data. This study explores the contribution of federated learning in building trustworthy AI systems, reducing the risk of data leakage by designing privacy protection mechanisms and enhancing data security measures, and enhancing the generalizability and robustness of models by using multi-party participation in the updating and validation of models. In addition, we have introduced a new way to assess the fairness of the model, aiming to achieve more fair and unbiased decision-making in a variety of use cases. In experimental analysis, our results show that by implementing these strategies, federated learning can significantly improve the transparency and fairness of AI systems, while achieving high model performance and low privacy risk on multiple cross-domain datasets. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM. |
| Author Keywords | Artificial Intelligence; Fairness; Federated Learning; Machine Learning |
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