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Federated learning methods for analytics of big and sensitive distributed data and survey
Title Federated learning methods for analytics of big and sensitive distributed data and survey Author Staňo Michal SAVINFO - Ústav informatiky SAV Co-authors Hluchý Ladislav 1952- SAVINFO - Ústav informatiky SAV SCOPUS RID ORCID Bobák Martin 1989- SAVINFO - Ústav informatiky SAV SCOPUS RID ORCID Krammer Peter 1984- SAVINFO - Ústav informatiky SAV SCOPUS RID ORCID Tran Viet 1972- SAVINFO - Ústav informatiky SAV SCOPUS RID ORCID Source document IEEE 17th international symposium on applied computational intelligence and informatics (SACI 2023) : Proceedings. P. 705-710. - Danvers, US : IEEE, 2023 Language eng - English Document kind rozpis článkov z periodík (rzb) Citations SHERPA, L. - BANERJI, N. Federated learning-hope and scope. In IgMin Research. 2023, vol. 1, no. 1, pp. 22-24. doi: 10.61927/igmin112. Category ADMB - Scientific papers in foreign non-impacted journals registered in Web of Sciences or Scopus Category of document (from 2022) V2 - Vedecký výstup publikačnej činnosti ako časť editovanej knihy alebo zborníka Type of document príspevok z podujatia Year 2023 Registered in SCOPUS DOI 10.1109/SACI58269.2023.10158622 article
rok vydania rok metriky IF IF Q (best) SJR SJR Q (best) 2023
Number of the records: 1