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DTSTAMP:20231220T083918Z
UID:E823F673-A20F-498A-AC90-A7DF00CBF73E
DTSTART;TZID=Europe/Bucharest:20231218T120000
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DESCRIPTION:Federated Learning (FL) is essential in the rapidly expanding I
 nternet-of-Things (IoT) landscape. The addition of nearly 2 billion device
 s in 2023 has intensified the need for Edge AI to adopt efficient FL solut
 ions\, thus enabling decentralized ML model training on edge devices with 
 a focus on data privacy.\n\nMy research focuses on Hierarchical Federated 
 Learning (HFL)\, a variant of FL that leverages edge servers that are phys
 ically closer to end-user devices. HFL is better fit for real-world scenar
 ios since it reduces communication costs and is more scalable.\n\nThis tal
 k will explore the synergies between hardware-software co-design and real-
 world considerations such as user mobility and semi-supervised learning so
 lutions in and FL\, showing how these innovations can lead to more efficie
 nt\, scalable\, and privacy-centric IoT ecosystems.\n\nBiography: Allen-Ja
 smin Farcas is a fourth-year Ph.D. student at The University of Texas at A
 ustin. His research interests include federated learning systems\, edge AI
  systems\, and hardware/software co-design for edge AI. Farcas received hi
 s bachelor’s degree in computer engineering in 2019 from Politehnica Uni
 versity Timisoara.\n\nSpeaker(s):  Allen-Jasmin Farcaș\, \n\nRoom: K1\, B
 ldg: Biblioteca Centrală a Universității Politehnica Timișoara\, Bulev
 ardul Vasile Pârvan 2\, Timișoara\, Timișoara\, Timis\, Romania
LOCATION:Room: K1\, Bldg: Biblioteca Centrală a Universității Politehnic
 a Timișoara\, Bulevardul Vasile Pârvan 2\, Timișoara\, Timișoara\, Tim
 is\, Romania
ORGANIZER:alexandru.topirceanu@cs.upt.ro
SEQUENCE:18
SUMMARY:Federated Learning for Edge AI: Realistic Hardware-Software System 
 Co-Design
URL;VALUE=URI:https://events.vtools.ieee.org/m/388768
X-ALT-DESC:Description: <br /><p>Federated Learning (FL) is essential in th
 e rapidly expanding Internet-of-Things (IoT) landscape. The addition of ne
 arly 2 billion devices in 2023 has intensified the need for Edge AI to ado
 pt efficient FL solutions\, thus enabling decentralized ML model training 
 on edge devices with a focus on data privacy.</p>\n<p>My research focuses 
 on Hierarchical Federated Learning (HFL)\, a variant of FL that leverages 
 edge servers that are physically closer to end-user devices. HFL is better
  fit for real-world scenarios since it reduces communication costs and is 
 more scalable.</p>\n<p>This talk will explore the synergies between hardwa
 re-software co-design and real-world considerations such as user mobility 
 and semi-supervised learning solutions in and FL\, showing how these innov
 ations can lead to more efficient\, scalable\, and privacy-centric IoT eco
 systems.</p>\n<p>Biography: Allen-Jasmin Farcas is a fourth-year Ph.D. stu
 dent at The University of Texas at Austin. His research interests include 
 federated learning systems\, edge AI systems\, and hardware/software co-de
 sign for edge AI. Farcas received his bachelor&rsquo\;s degree in computer
  engineering in 2019 from Politehnica University Timisoara.</p>
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