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PRODID:IEEE vTools.Events//EN
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TZID:Europe/Berlin
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DTSTART:20260329T030000
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DTSTART:20251026T020000
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BEGIN:VEVENT
DTSTAMP:20260225T151715Z
UID:A608AF23-DDD0-4521-9999-1A4FA4D504E5
DTSTART;TZID=Europe/Berlin:20260120T170000
DTEND;TZID=Europe/Berlin:20260120T190000
DESCRIPTION:We will discuss the challenges and opportunities of distributed
  data management solutions ranging from the mobile edge to the data center
 s. Modern 5G networks promise to provide all means for communication in th
 is domain\, particularly when integrating Mobile Edge Computing (MEC). How
 ever\, it turns out that despite the many advantages\, it is unlikely that
  such services will be provided with sufficient coverage. As a novel conce
 pt\, virtualized edge computing (V-Edge) have been proposed that bridges t
 his gap. We present a learning-based approach to make such an V-Edge resil
 ient to dynamics\, failures\, and even malicious attacks. In particular\, 
 we contrast centralized and federated learning approaches and reinforcemen
 t based approaches.\n\nSpeaker(s): Prof. Dr. Falko Dressler\n\nVirtual: ht
 tps://events.vtools.ieee.org/m/532101
LOCATION:Virtual: https://events.vtools.ieee.org/m/532101
ORGANIZER:heracletus@hotmail.com
SEQUENCE:39
SUMMARY:Webinar: "Virtualized Edge Computing in the Vehicular Micro Cloud"
URL;VALUE=URI:https://events.vtools.ieee.org/m/532101
X-ALT-DESC:Description: <br /><p>We will discuss the challenges and opportu
 nities of distributed data management solutions ranging from the mobile ed
 ge to the data centers. Modern 5G networks promise to provide all means fo
 r communication in this domain\, particularly when integrating Mobile Edge
  Computing (MEC). However\, it turns out that despite the many advantages\
 , it is unlikely that such services will be provided with sufficient cover
 age. As a novel concept\, virtualized edge computing (V-Edge) have been pr
 oposed that bridges this gap. We present a learning-based approach to make
  such an V-Edge resilient to dynamics\, failures\, and even malicious atta
 cks. In particular\, we contrast centralized and federated learning approa
 ches and reinforcement based approaches.</p>
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