BEGIN:VCALENDAR METHOD:REQUEST PRODID:Microsoft Exchange Server 2010 VERSION:2.0 BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD DTSTART:16010101T000000 TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010101T000000 TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ORGANIZER;CN=Evgeny Osipov:mailto:evgeny.osipov@ltu.se ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN='Google Gr oups' via VSACommunity:mailto:vsacommunity@googlegroups.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Parikshit Ram:mailto:Parikshit.Ram@ibm.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Daswin De Silva:mailto:D.DeSilva@latrobe.edu.au ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Fredrick A ngelo Galapon:mailto:fredrick.galapon@eee.upd.edu.ph ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Cynamon, J osh":mailto:jcynamon@iu.edu ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="MCDONALD, NATHAN R CIV USAF AFMC AFRL/RITB":mailto:nathan.mcdonald.5@us.af.mil ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Ibrahim, M ohamed":mailto:mibrahim@utdallas.edu ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Ross Gayle r:mailto:r.gayler@gmail.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=GEETH R DE MEL:mailto:geeth.demel@uk.ibm.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Marco Angi oli:mailto:marco.angioli@uniroma1.it ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Peter Bruz a:mailto:p.bruza@qut.edu.au ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Colyn Seel ey:mailto:colynseeley218@gmail.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Jesper Ols en:mailto:jesper.olsen@gmail.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Wanyu Lei: mailto:wlei@berkeley.edu ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Leonid Mok rushin:mailto:leonid.mokrushin@ericsson.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Rocco Mart ino:mailto:rocco.martino@uniroma1.it ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Trevor Coh en:mailto:treversec@gmail.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Dave Bende r:mailto:davecbender@gmail.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Dmitri Rac hkovskij:mailto:dar@infrm.kiev.ua ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Giacomo Ca mposampiero:mailto:Giacomo.Camposampiero1@ibm.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Fatemeh As garinejad:mailto:fasgarinejad@ucsd.edu ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Paxon Frad y:mailto:paxon.frady@gmail.com ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=n2mills:ma ilto:n2mills@ltu.edu.au ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Sadrzadeh, Mehrnoosh":mailto:m.sadrzadeh@ucl.ac.uk ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Antonello Rosato:mailto:antonello.rosato@uniroma1.it ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Ilya Pavel iev:mailto:paveliei@tcd.ie ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Ye Tian:ma ilto:yet002@ucsd.edu ATTENDEE;ROLE=OPT-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Tajana Ros ing:mailto:tajana@ucsd.edu DESCRIPTION;LANGUAGE=sv-SE:Dear all\,\nThis is a reminder about the VSAONLI NE webinars today as per details below.\nSee you all in couple of hours!\n Evgeny\n----\n Welcome to the next talk of Season 11 on VSAONLINE. Ye Tian \, UC San Diego\, USA will deliver a talk\n”Federated Hyperdimensional C omputing: Comprehensive Analysis and Robust Communication”\nDate: Octobe r 20\, 2025\nTime: 20:00 GMT\nZoom: https://ltu-se.zoom.us/j/65564790287\ n\nWEB: https://bit.ly/vsaonline\nTitle: Federated Hyperdimensional Compu ting: Comprehensive Analysis and Robust Communication.\nAbstract: Federat ed learning is a distributed learning method by training the model in loca lly multiple clients\, which has been used in numerous fields. Current con volutional neural networks (CNN)-based federated learning approaches face challenges from computational cost\, communication efficiency\, and robust communication. Recently\, Hyper Dimensional Computing (HDC) has been reco gnized as a promising technique to address these challenges. HDC encodes d ata as high-dimensional vectors and enables lightweight training and commu nication through simple parallel vector operations. Several HDC-based fede rated learning methods have been proposed. Although existing methods reduc e computational efficiency and communication cost\, they are difficult to handle complex learning tasks and are not robust to unreliable wireless ch annels. In this work\, we innovatively introduce a synergetic federated le arning framework\, FHDnn. With the complementary strengths of CNN and HDC\ , FHDnn can achieve optimal performance on complex image tasks while maint aining good computational and communication efficiency. Secondly\, we demo nstrate in detail the convergence of using HDC in a generalized federated learning framework\, providing theoretical guarantees for HDC-based federa ted learning approach. Finally\, we design three communication strategies to further improve the communication efficiency of FHDnn by 32×. Experime nts demonstrate that FHDnn converges 3× faster than CNN-based federated l earning methods\, reduces the communication cost by 2\,112×\, and the loc al computation and energy consumption by 192×. In addition\, it has good robustness to unreliable communication with bit errors\, noise\, and packe t loss.\n UID:040000008200E00074C5B7101A82E00800000000794880C06D08DC01000000000000000 0100000003C52A1DB3A206D4881905B5B8FB19D51 SUMMARY;LANGUAGE=sv-SE:VSAONLINE. SEASON 11. October 20\, 20:00GMT. Ye Tia n. 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