Federal learning for security and privacy protection

Bokai Zhao

doi:10.1117/12.2675351

25 May 2023 Federal learning for security and privacy protection

Bokai Zhao

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126360L (2023) https://doi.org/10.1117/12.2675351
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Federated learning empowers different gatherings to coach a machine learning model together whilst not trading their local data, and give full play to the worth of data from all parties because of the inborn deficiencies of federated learning and the security issues of information stockpiling and correspondence, it still faces a variety of security and privacy threats in practical application scenarios the security attacks and privacy attacks looked by federal learning are described. Then, at that point, the most recent security defense mechanisms and privacy protection means are summed up for such run of the typical attacks, including defense against poisoning attacks, protection against adversarial attack, defense against Free-rider attack, and defense against Sybil attack. At last, by efficiently figuring out the current risks and comparing the defense method for federal learning, the application of existing privacy protection methods to federated learning is discussed and the future exploration difficulties and advancement headings of federal learning are predicted.

Citation Download Citation

Bokai Zhao "Federal learning for security and privacy protection", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126360L (25 May 2023); https://doi.org/10.1117/12.2675351

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