{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T11:54:14Z","timestamp":1743076454078,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811591280"},{"type":"electronic","value":"9789811591297"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-9129-7_40","type":"book-chapter","created":{"date-parts":[[2020,10,21]],"date-time":"2020-10-21T23:07:29Z","timestamp":1603321649000},"page":"577-590","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Communication Efficiency for Encrypted Distributed Training"],"prefix":"10.1007","author":[{"given":"Minglu","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Qixian","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Shaopeng","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Haomiao","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,22]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Xing, E.P., et al.: Petuum: a new platform for distributed machine learning on big data. IEEE Trans. Big Data 1(2), 49\u201367 2015","DOI":"10.1109\/TBDATA.2015.2472014"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Wang, Z., Song, M., Zhang, Z., et al.: Beyond inferring class representatives: user-level privacy leakage from federated learning. In: 2019-IEEE Conference on Computer Communications, pp. 2512-2520. IEEE (2019)","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"40_CR3","doi-asserted-by":"crossref","unstructured":"Melis, L., et al.: Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE Symposium on Security and Privacy (SP). IEEE (2019)","DOI":"10.1109\/SP.2019.00029"},{"key":"40_CR4","unstructured":"https:\/\/github.com\/OpenMined\/PySyft"},{"key":"40_CR5","unstructured":"Ryffel, T., et al.: A generic framework for privacy preserving deep learning.\u00a0arXiv preprint arXiv:1811.04017 (2018)"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Bogdanov, D., et al.: High-performance secure multi-party computation for data mining applications. Int. J. Inf. Secur.\u00a011(6), 403\u2013418 (2012)","DOI":"10.1007\/s10207-012-0177-2"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Ma, X., et al.: Privacy preserving multi-party computation delegation for deep learning in cloud computing.\u00a0Inf. Sci.\u00a0459, 103\u2013116 (2018)","DOI":"10.1016\/j.ins.2018.05.005"},{"key":"40_CR8","unstructured":"Chase, M., et al.: Private collaborative neural network learning.\u00a0IACR Cryptology ePrint Archive\u00a02017, p. 762 (2017)"},{"key":"40_CR9","unstructured":"Speedtest.net: Speedtest market report, March 2020. https:\/\/www.speedtest.net\/global-index#mobile"},{"key":"40_CR10","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)"},{"key":"40_CR11","unstructured":"Vogels, T., Karimireddy, S.P., Jaggi, M.: PowerSGD: practical low-rank gradient compression for distributed optimization.\u00a0In: Advances in Neural Information Processing Systems (2019)"},{"key":"40_CR12","unstructured":"Kone\u010dn\u00fd, J., et al.: Federated learning: strategies for improving communication efficiency.\u00a0arXiv preprint arXiv:1610.05492 (2016)"},{"key":"40_CR13","unstructured":"Alistarh, D., et al.: QSGD: communication-efficient SGD via gradient quantization and encoding. In: Advances in Neural Information Processing Systems (2017)"},{"key":"40_CR14","unstructured":"Lin, Y., et al.: Deep gradient compression: reducing the communication bandwidth for distributed training. In: International Conference on Learning Representations (ICLR) (2018)"},{"key":"40_CR15","unstructured":"Ryffel, T., et al.: A generic framework for privacy preserving deep learning.\u00a0arXiv preprint arXiv:1811.04017 (2018)"},{"issue":"1","key":"40_CR16","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"Ning Qian","year":"1999","unstructured":"Qian, Ning: On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145\u2013151 (1999)","journal-title":"Neural Netw."},{"key":"40_CR17","unstructured":"Goyal, P., et al.: Accurate, large minibatch sgd: Training imagenet in 1 hour.\u00a0arXiv preprint arXiv:1706.02677 (2017)"},{"key":"40_CR18","unstructured":"https:\/\/github.com\/torch\/nn"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"40_CR20","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: iDLG: improved deep leakage from gradients.\u00a0arXiv preprint arXiv:2001.02610 (2020)"}],"container-title":["Communications in Computer and Information Science","Security and Privacy in Digital Economy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-9129-7_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T02:40:11Z","timestamp":1619318411000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-15-9129-7_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811591280","9789811591297"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-9129-7_40","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"22 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SPDE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Security and Privacy in Digital Economy","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Quzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"spde2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/spde2020.csp.escience.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"132","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}