{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:46:15Z","timestamp":1742949975654,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030859275"},{"type":"electronic","value":"9783030859282"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-85928-2_44","type":"book-chapter","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T12:12:16Z","timestamp":1631103136000},"page":"559-571","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Communication-efficient Federated Learning via Quantized Clipped SGD"],"prefix":"10.1007","author":[{"given":"Ninghui","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Qu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baoliu","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,2]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Aji, A.F., Heafield, K.: Sparse communication for distributed gradient descent. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 440\u2013445 (2017)","DOI":"10.18653\/v1\/D17-1045"},{"key":"44_CR2","unstructured":"Alistarh, D., Grubic, D., Li, J., Tomioka, R., Vojnovic, M.: QSGD: communication-efficient SGD via gradient quantization and encoding. In: Advances in Neural Information Processing Systems, vol. 30, pp. 1709\u20131720 (2017)"},{"key":"44_CR3","unstructured":"Alistarh, D., Hoefler, T., Johansson, M., Konstantinov, N., Khirirat, S., Renggli, C.: The convergence of Sparsified Gradient methods. In: NeurIPS (2018)"},{"key":"44_CR4","unstructured":"Bernstein, J., Zhao, J., Azizzadenesheli, K., Anandkumar, A.: SignSGD with majority vote is communication efficient and fault tolerant. arXiv preprint arXiv:1810.05291 (2018)"},{"key":"44_CR5","doi-asserted-by":"crossref","unstructured":"Chen, C.Y., Choi, J., Brand, D., Agrawal, A., Zhang, W., Gopalakrishnan, K.: ADaComP: adaptive residual gradient compression for data-parallel distributed training. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11728"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H.V., Cui, S.: A joint learning and communications framework for federated learning over wireless networks. IEEE Trans. Wireless Commun. (2020)","DOI":"10.1109\/GLOBECOM38437.2019.9013160"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Han, P., Wang, S., Leung, K.K.: Adaptive gradient sparsification for efficient federated learning: an online learning approach. arXiv preprint arXiv:2001.04756 (2020)","DOI":"10.1109\/ICDCS47774.2020.00026"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Huang, T., Ye, B., Qu, Z., Tang, B., Xie, L., Lu, S.: Physical-layer arithmetic for federated learning in uplink MU-MIMO enabled wireless networks. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 1221\u20131230. IEEE (2020)","DOI":"10.1109\/INFOCOM41043.2020.9155479"},{"key":"44_CR9","unstructured":"Jia, N.: https:\/\/github.com\/jianinghui\/WASA2021.git"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Jiang, J., Fu, F., Yang, T., Cui, B.: SketchML: accelerating distributed machine learning with data sketches. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1269\u20131284 (2018)","DOI":"10.1145\/3183713.3196894"},{"key":"44_CR11","unstructured":"Kairouz, P., et al.: Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)"},{"key":"44_CR12","unstructured":"Lin, Y., Han, S., Mao, H., Wang, Y., Dally, B.: Deep gradient compression: reducing the communication bandwidth for distributed training. In: International Conference on Learning Representations (2018)"},{"key":"44_CR13","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"44_CR14","unstructured":"Murata, T., Suzuki, T.: Accelerated Sparsified SGD with Error Feedback. arXiv preprint arXiv:1905.12224 (2019)"},{"key":"44_CR15","unstructured":"Rothchild, D., et al.: FetchSGD: communication-efficient federated learning with sketching. In: International Conference on Machine Learning, pp. 8253\u20138265. PMLR (2020)"},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Seide, F., Fu, H., Droppo, J., Li, G., Yu, D.: 1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)","DOI":"10.21437\/Interspeech.2014-274"},{"key":"44_CR17","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, pp. 1310\u20131321 (2015)","DOI":"10.1145\/2810103.2813687"},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"Tran, N.H., Bao, W., Zomaya, A., Nguyen, M.N., Hong, C.S.: Federated learning over wireless networks: optimization model design and analysis. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 1387\u20131395. IEEE (2019)","DOI":"10.1109\/INFOCOM.2019.8737464"},{"issue":"6","key":"44_CR19","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","volume":"37","author":"S Wang","year":"2019","unstructured":"Wang, S., et al.: Adaptive federated learning in resource constrained edge computing systems. IEEE J. Sel. Areas Commun. 37(6), 1205\u20131221 (2019)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"44_CR20","unstructured":"Wen, W., et al.: TernGrad: ternary gradients to reduce communication in distributed deep learning. arXiv preprint arXiv:1705.07878 (2017)"},{"key":"44_CR21","unstructured":"Wu, J., Huang, W., Huang, J., Zhang, T.: Error compensated quantized SGD and its applications to large-scale distributed optimization. In: International Conference on Machine Learning, pp. 5325\u20135333. PMLR (2018)"},{"key":"44_CR22","unstructured":"Zhang, J., He, T., Sra, S., Jadbabaie, A.: Why gradient clipping accelerates training: a theoretical justification for adaptivity. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=BJgnXpVYwS"},{"key":"44_CR23","unstructured":"Zhao, S.Y., Xie, Y.P., Gao, H., Li, W.J.: Global momentum compression for sparse communication in distributed SGD. arXiv preprint arXiv:1905.12948 (2019)"}],"container-title":["Lecture Notes in Computer Science","Wireless Algorithms, Systems, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-85928-2_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,8]],"date-time":"2023-01-08T21:00:38Z","timestamp":1673211638000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-85928-2_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030859275","9783030859282"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-85928-2_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WASA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless Algorithms, Systems, and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wasa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wasa-conference.org\/WASA2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","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":"315","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":"103","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":"57","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":"33% - 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":"6","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)"}}]}}