BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv7.32.0//EN
X-ORIGINAL-URL:https://dcn.nat.fau.eu/
X-WR-CALNAME:
X-WR-CALDESC:FAU DCN-AvH. Chair for Dynamics, Control, Machine Learning and Numerics -Alexander von Humboldt Professorship
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VTIMEZONE
TZID:Europe/Berlin
X-LIC-LOCATION:Europe/Berlin
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T030000
RRULE:FREQ=YEARLY;BYMONTH=03;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=4SU
END:STANDARD
END:VTIMEZONE
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-14253cc3324fa4766ffbe5f12a7dba10@dcn.nat.fau.eu
DTSTART;TZID=Europe/Berlin:20240425T113000
DTEND;TZID=Europe/Berlin:20240425T123000
DTSTAMP:20240423T124402Z
CREATED:20240423
LAST-MODIFIED:20240507
PRIORITY:5
SEQUENCE:0
TRANSP:OPAQUE
SUMMARY:Efficient federated learning via the alternating direction method of multipliers
DESCRIPTION:Next Thursday April 25, 2024:\nOrganized by: FAU DCN-AvH, Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship at FAU, Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)\nTitle: Efficient federated learning via the alternating direction method of multipliers\nSpeaker: Ziqi Wang\nAffiliation: PhD student at FAU DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship.\nAbstract.  Federated Learning (FL) has emerged as a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challenges, including heterogeneous data and systems, limited communication capacities, and constrained local computational resources. In this talk, we introduce a novel approach leveraging the alternating direction method of multipliers (ADMM) to address these challenges. Our inexact and self-adaptive FedADMM algorithm mitigates the necessity for empirical hyperparameter settings. The convergence of the proposed algorithm using the inexactness criterion is analyzed, and numerical experiments demonstrate the enhanced performance of our algorithm.\nWHEN\nThu. April 25, 2024 at 11:30H\nWHERE\nOn-site: Room 03.323\nFriedrich-Alexander-Universität Erlangen-Nürnberg\nCauerstraße 11, 91058 Erlangen\nGPS-Koord. Raum: 49.573764N, 11.030028E\n_\nSee all Seminars at FAU DCN-AvH\nDon’t miss out our last news and connect with us!\nLinkedIn | Twitter | Instagram\n
URL:https://dcn.nat.fau.eu/events/fau-dcn-avh-jr-25-abr-2024/
ORGANIZER;CN=FAU DCN-AvH:MAILTO:
CATEGORIES:FAU DCN-AvH Jr. Seminar
ATTACH;FMTTYPE=image/png:https://dcn.nat.fau.eu/wp-content/uploads/FAUDCNAvHJrSeminar_zWang_25apr2024.png
END:VEVENT
END:VCALENDAR