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DESCRIPTION:Abstract\nFog learning is an emerging paradigm for optimizing t
 he orchestration of artificial intelligence services over contemporary net
 work systems. Different from existing distributed techniques such as feder
 ated learning\, fog learning emphasizes intrinsically in its design the un
 ique node\, network\, and data properties encountered in today’s fog net
 works that span computing elements from the edge to the cloud. An importan
 t thread of research in fog learning has been on understanding the role th
 at local topologies formed on an ad-hoc basis among proximal groups of het
 erogeneous computing elements can play in elevating the achievable tradeof
 f between intelligence quality and resource efficiency. In this talk\, I w
 ill discuss recent results on the analysis of fog learning processes which
  give insights into the impact that these topologies\, along with other pr
 operties such as model characteristics and fog decision parameters\, have 
 on global training performance. Additionally\, I will discuss the developm
 ent of adaptive control methodologies that leverage such relationships for
  jointly optimizing relevant fog learning metrics.\n\nDistinguished Lectur
 er Series: https://www.comsoc.org/membership/distinguished-lecturers\nSpea
 ker: https://www.comsoc.org/christopher-greg-brinton\n\nCo-sponsored by: N
 orth Jersey Information Theory Chapter\n\nSpeaker(s): Chris Brinton\, \n\n
 Agenda: \n6:30-7:00pm Gather\, Refreshments and Introduction\n7:00-8:00pm 
 Lecture\n8:00-8:30pm Q&amp;A\, networking\, wrap-up\n\nRoom: Main Auditorium\,
  Bldg: CAIT (Center For Advanced Infra And Transport) near ECE Bldg - Lot#
 59\, Rutgers University - Busch Campus\, 100 Brett Road\, Piscataway\, New
  Jersey\, United States\, 08854-8058
LOCATION:Room: Main Auditorium\, Bldg: CAIT (Center For Advanced Infra And 
 Transport) near ECE Bldg - Lot#59\, Rutgers University - Busch Campus\, 10
 0 Brett Road\, Piscataway\, New Jersey\, United States\, 08854-8058
ORGANIZER:a.j.patel@ieee.org
SEQUENCE:18
SUMMARY:From Federated to Fog Learning: Expanding the Frontier of Model Tra
 ining over Contemporary Wireless Network Systems
URL;VALUE=URI:https://events.vtools.ieee.org/m/479957
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Abstract&lt;br&gt;Fog learning is an emerging pa
 radigm for optimizing the orchestration of artificial intelligence service
 s over contemporary network systems. Different from existing distributed t
 echniques such as federated learning\, fog learning emphasizes intrinsical
 ly in its design the unique node\, network\, and data properties encounter
 ed in today&amp;rsquo\;s fog networks that span computing elements from the ed
 ge to the cloud. An important thread of research in fog learning has been 
 on understanding the role that local topologies formed on an ad-hoc basis 
 among proximal groups of heterogeneous computing elements can play in elev
 ating the achievable tradeoff between intelligence quality and resource ef
 ficiency. In this talk\, I will discuss recent results on the analysis of 
 fog learning processes which give insights into the impact that these topo
 logies\, along with other properties such as model characteristics and fog
  decision parameters\, have on global training performance. Additionally\,
  I will discuss the development of adaptive control methodologies that lev
 erage such relationships for jointly optimizing relevant fog learning metr
 ics.&lt;/p&gt;\n&lt;p&gt;Distinguished Lecturer Series:&amp;nbsp\;&lt;a href=&quot;https://www.com
 soc.org/membership/distinguished-lecturers&quot;&gt;https://www.comsoc.org/members
 hip/distinguished-lecturers&lt;/a&gt;&lt;br&gt;Speaker: https://www.comsoc.org/christo
 pher-greg-brinton&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p&gt;6:30-7:0
 0pm&amp;nbsp\; Gather\, Refreshments and Introduction&lt;br&gt;7:00-8:00pm&amp;nbsp\; Le
 cture&lt;br&gt;8:00-8:30pm&amp;nbsp\; Q&amp;amp\;A\, networking\, wrap-up&lt;/p&gt;
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