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DESCRIPTION:[]FAMU-FSU College of Engineering &amp; IEEE Tallahassee Section\, 
 PES Chapter\, LMAG Joint Technical Seminar\n\nTitle: Distributed Machine L
 earning over Wireless Networks: From Classical to Quantum Approaches\n\nSp
 eaker: Dr. Dinh C. Nguyen\, Department of Electrical and Computer Engineer
 ing\, University of Alabama in Huntsville\n\nDate: Friday\, December 5\, 2
 025\, 11:00 – 12:00\n\nLocation: College of Engineering\, FAMU-FSU\n\nAb
 stract\n\nThe emergence of distributed machine learning has transformed ho
 w intelligent systems are designed and deployed across wireless networks. 
 This talk will explore the progression of Federated Learning (FL) and its 
 quantum counterpart toward developing scalable\, secure\, and efficient di
 stributed intelligence in wireless systems. The talk will consist of two m
 ain parts. The first part focuses on FL over wireless networks\, emphasizi
 ng key design aspects such as communication resource allocation and securi
 ty mechanisms to ensure reliable machine learning model training under rea
 listic network constraints. The second part expands the discussion to Quan
 tum Federated Learning (QFL)\, addressing the challenges posed by limited 
 qubit counts in near-term quantum devices through innovative qubit reuse s
 trategies and enhanced model explainability to improve transparency and tr
 ust in quantum-driven learning systems. Real-world applications of FL and 
 QFL will be showcased in Internet of Things (IoT) applications including s
 mart grid. Finally\, we will outline open challenges and promising researc
 h directions toward achieving trustworthy\, high-performance\, and quantum
 -driven distributed learning over wireless networks.\n\nSpeaker(s): Dr.Nqu
 yen\, \n\nBldg: Florida A&amp;M University-Florida State University\, 2525 Pot
 tsdamer St\, Tallahassee\, Florida\, United States\, 32310
LOCATION:Bldg: Florida A&amp;M University-Florida State University\, 2525 Potts
 damer St\, Tallahassee\, Florida\, United States\, 32310
ORGANIZER:ckim@caps.fsu.edu
SEQUENCE:11
SUMMARY:Distributed Machine Learning over Wireless Networks: From Classical
  to Quantum Approaches
URL;VALUE=URI:https://events.vtools.ieee.org/m/516252
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: 0i
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 ng&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;FAMU-FSU College 
 of Engineering &amp;amp\; IEEE Tallahassee Section\, PES Chapter\, LMAG Joint 
 Technical Seminar&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-
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 &quot;&gt;Title: Distributed Machine Learning over Wireless Networks: From Classic
 al to Quantum Approaches&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;ma
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 942031\;&quot;&gt;&lt;span style=&quot;font-family: Roboto\; mso-fareast-font-family: Robo
 to\; mso-bidi-font-family: Roboto\;&quot;&gt;Speaker: Dr. Dinh C. Nguyen\, Departm
 ent of Electrical and Computer Engineering\, University of Alabama in Hunt
 sville&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: 0in\;
  line-height: 115%\;&quot;&gt;&lt;span style=&quot;mso-bookmark: _Hlk199942031\;&quot;&gt;&lt;span st
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 -family: Roboto\;&quot;&gt;Date: Friday\, December 5\, 2025\, 11:00 &amp;ndash\; 12:00
 &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-bottom: 0in\; line-
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 y: Roboto\;&quot;&gt;Location: College of Engineering\, FAMU-FSU&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
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 enter&quot;&gt;&lt;strong&gt;&lt;span style=&quot;font-family: &#39;Times New Roman&#39;\,serif\;&quot;&gt;Abstr
 act&lt;/span&gt;&lt;/strong&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: justify\;
 &quot;&gt;&lt;span style=&quot;font-family: Roboto\; mso-fareast-font-family: Roboto\; mso
 -bidi-font-family: Roboto\;&quot;&gt;The emergence of distributed machine learning
  has transformed how intelligent systems are designed and deployed across 
 wireless networks. This talk will explore the progression of Federated Lea
 rning (FL) and its quantum counterpart toward developing scalable\, secure
 \, and efficient distributed intelligence in wireless systems. The talk wi
 ll consist of two main parts. The first part focuses on FL over wireless n
 etworks\, emphasizing key design aspects such as communication resource al
 location and security mechanisms to ensure reliable machine learning model
  training under realistic network constraints. The second part expands the
  discussion to Quantum Federated Learning (QFL)\, addressing the challenge
 s posed by limited qubit counts in near-term quantum devices through innov
 ative qubit reuse strategies and enhanced model explainability to improve 
 transparency and trust in quantum-driven learning systems. Real-world appl
 ications of FL and QFL will be showcased in Internet of Things (IoT) appli
 cations including smart grid. Finally\, we will outline open challenges an
 d promising research directions toward achieving trustworthy\, high-perfor
 mance\, and quantum-driven distributed learning over wireless networks.&lt;/s
 pan&gt;&lt;/p&gt;
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