Federated learning methods for analytics of big and sensitive distributed data and survey

Number of the records: 1

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

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rok vydania	rok metriky	IF	IF Q (best)	SJR	SJR Q (best)
2023

Number of the records: 1