{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:04:47Z","timestamp":1743015887639,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031302572"},{"type":"electronic","value":"9783031302589"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-30258-9_47","type":"book-chapter","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T07:09:12Z","timestamp":1681888152000},"page":"525-533","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Federated Learning Strategies Over Wireless Channels"],"prefix":"10.1007","author":[{"given":"Amjad","family":"Ali","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"47_CR1","doi-asserted-by":"crossref","unstructured":"Savazzi, S., Nicoli, M., Bennis, M., Kianoush, S., Barbieri, L.: Opportunities of federated learning in connected, cooperative and automated industrial systems. IEEE Trans. Wireless Comm. 59(29) (2021)","DOI":"10.1109\/MCOM.001.2000200"},{"key":"47_CR2","doi-asserted-by":"publisher","first-page":"140699","DOI":"10.1109\/ACCESS.2020.3013541","volume":"8","author":"M Aledhari","year":"2020","unstructured":"Aledhari, M., Razzak, R., Parizi, R.M., Saeed, F.: Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access 8, 140699\u2013140725 (2020)","journal-title":"IEEE Access"},{"key":"47_CR3","doi-asserted-by":"crossref","unstructured":"Sery, T., Shlezinger, N., Cohen, K., Eldar, Y.C.: Over-the-air federated learning from heterogeneous data. IEEE Trans. Signal Process. 69 (2020)","DOI":"10.1109\/TSP.2021.3090323"},{"key":"47_CR4","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1561\/2200000051","volume":"7","author":"AH Sayed","year":"2014","unstructured":"Sayed, A.H.: Adaptation, learning, and optimization over networks. Found. Trends Mach. Learn. 7, 311\u2013801 (2014)","journal-title":"Found. Trends Mach. Learn."},{"key":"47_CR5","doi-asserted-by":"crossref","unstructured":"Doku, R., Rawat, D.B., Liu, C.: Towards federated learning approach to determine data relevance in big data. In: IEEE 20th International Conference on Information Reuse and Integration for D ata Science (IRI), pp. 184\u2013192, Los Angeles, USA (2019)","DOI":"10.1109\/IRI.2019.00039"},{"key":"47_CR6","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)"},{"key":"47_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: FedCoin: a peer-to-peer payment system for federated learning. arXiv preprint arXiv:2002.11711 (2020)","DOI":"10.1007\/978-3-030-63076-8_9"},{"key":"47_CR8","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132\u20135143. PMLR (2020)"},{"key":"47_CR9","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neucom.2018.11.002","volume":"330","author":"M Blot","year":"2019","unstructured":"Blot, M., Picard, D., Thome, N., Cord, M.: Distributed optimization for deep learning with gossip exchange. Neurocomputing 330, 287\u2013296 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2018.11.002","journal-title":"Neurocomputing"},{"key":"47_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2964162","author":"S Savazzi","year":"2020","unstructured":"Savazzi, S., Nicoli, M., Rampa, V.: Federated learning with cooperating devices: a consensus approach for massive IoT networks. IEEE Internet Things J. (2020). https:\/\/doi.org\/10.1109\/JIOT.2020.2964162","journal-title":"IEEE Internet Things J."},{"key":"47_CR11","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1109\/MSP.2012.2231991","volume":"30","author":"AH Sayed","year":"2013","unstructured":"Sayed, A.H., Tu, S.Y., Chen, J., Zhao, X.: Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior. IEEE Signal Proces. Mag. 30, 155\u2013171 (2013)","journal-title":"IEEE Signal Proces. Mag."}],"container-title":["Lecture Notes in Computer Science","Internet of Things, Smart Spaces, and Next Generation Networks and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30258-9_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T07:26:24Z","timestamp":1681889184000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30258-9_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031302572","9783031302589"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30258-9_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"20 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NEW2AN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Next Generation Wired\/Wireless Networking","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tashkent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Uzbekistan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"new2an2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/new2an.info\/index.html","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":"EDAS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"282","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":"58","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":"0","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":"21% - 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":"4.6","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}