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University","award":["GDJX2020009"],"award-info":[{"award-number":["GDJX2020009"]}]},{"name":"Information Security Teaching Reform Project of Wuyi University","award":["JX2020052"],"award-info":[{"award-number":["JX2020052"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Federated learning is a distributed machine learning framework, which allows users to save data locally for training without sharing data. Users send the trained local model to the server for aggregation. However, untrusted servers may infer users\u2019 private information from the provided data and mistakenly execute aggregation protocols to forge aggregation results. In order to ensure the reliability of the federated learning scheme, we must protect the privacy of users\u2019 information and ensure the integrity of the aggregation results. This paper proposes an effective secure aggregation verifiable federated learning scheme, which has both high communication efficiency and privacy protection function. The scheme encrypts the gradients with a single mask technology to securely aggregate gradients, thus ensuring that malicious servers cannot deduce users\u2019 private information from the provided data. Then the masked gradients are hashed to verify the aggregation results. The experimental results show that our protocol is more suited for bandwidth-constraint and offline-users scenarios.<\/jats:p>","DOI":"10.3390\/e25081125","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T01:51:47Z","timestamp":1690509107000},"page":"1125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning"],"prefix":"10.3390","volume":"25","author":[{"given":"Kaixin","family":"Peng","sequence":"first","affiliation":[{"name":"The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Shen","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"},{"name":"The Key Laboratory of Cryptography of Zhejiang Province, Hangzhou Normal University, Hangzhou 311121, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3410-2307","authenticated-orcid":false,"given":"Le","family":"Gao","sequence":"additional","affiliation":[{"name":"The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baocang","family":"Wang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Integrated Service Networks, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichao","family":"Lu","sequence":"additional","affiliation":[{"name":"The Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106854","DOI":"10.1016\/j.cie.2020.106854","article-title":"A review of applications in federated learning","volume":"149","author":"Li","year":"2020","journal-title":"Comput. 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