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DESCRIPTION:Smart assistive environments adapt to the needs and preferences
  of disabled or elderly users who need help with the activities of daily l
 iving. However\, the needs and requests of users vary greatly\, both due t
 o personal preferences and type of disability. As handcrafting an environm
 ent is prohibitively expensive\, in recent years significant research was 
 done in systems that use machine learning to create a predictive model of 
 the user. Machine learning\, however\, typically requires large amounts of
  data. A stand-alone smart environment\, however\, only has access to the 
 data collected from its user since it was deployed. A possible solution is
  to perform centralized\, cloud-based learning by pooling the training dat
 a collected from multiple users. However\, uploading data collected from t
 he personal habits of elderly and disabled users create significant securi
 ty and privacy concerns.\n\nIn this talk\, we investigate the type of data
  sharing necessary for learning user models in smart environments and prop
 ose several novel considerations. We point out that data sharing is only e
 thical if the user derives a benefit from it. This implies that the decisi
 on to share data must be periodically revisited\, it is not a commitment e
 xtending indefinitely in the future. We study the data sharing decisions m
 ade by users under several machine learning frameworks: local\, cloud\, an
 d federated learning. We show that most users only benefit from data shari
 ng for a limited interval after the deployment of the system. We also inve
 stigate machine learning techniques that predict whether the user will ben
 efit from sharing the data before the data is shared.\n\nCo-sponsored by: 
 Tamseel Mahmood - syed.tamseel@ieee.org\n\nSpeaker(s): Dr. Damla Turgut\, 
 \n\nAgenda: \nBio: Dr. Turgut is Charles Millican Professor of Computer Sc
 ience at the University of Central Florida (UCF). She is the co-director o
 f the AI Things Laboratory. She held visiting researcher positions at the 
 University of Rome ``La Sapienza''\, Imperial College of London\, and KTH 
 Royal Institute of Technology\, Sweden. Her research interests include wir
 eless ad hoc\, sensor\, underwater\, vehicular\, and social networks\, edg
 e/cloud computing\, smart cities\, smart grids\, IoT-enabled healthcare an
 d augmented reality\, as well as considerations of privacy in the Internet
  of Things. Dr. Turgut serves on several editorial boards and program comm
 ittees of prestigious ACM and IEEE journals and conferences. Her most rece
 nt honors include the NCWIT 2021 Mentoring Award for Undergraduate Researc
 h (MAUR)\, the UCF Research Incentive Award\, and the UCF Women of Distinc
 tion Award. Since 2019\, she serves as the N2Women Board Co-Chair where sh
 e co-leads the activities of the N2Women Board in supporting female resear
 chers in the fields of networking and communications. She is an IEEE ComSo
 c Distinguished Lecturer\, IEEE Senior Member\, and the Chair-Elect of the
  IEEE Technical Committee on Computer Communications (TCCC).\n\nVirtual: h
 ttps://events.vtools.ieee.org/m/286611
LOCATION:Virtual: https://events.vtools.ieee.org/m/286611
ORGANIZER:bpage1@ieee.org
SEQUENCE:9
SUMMARY:VDL Dr. Turgut Communication\, computation\, and privacy trade-off 
 in machine learning for smart environments
URL;VALUE=URI:https://events.vtools.ieee.org/m/286611
X-ALT-DESC:Description: <br /><p>Smart assistive environments adapt to the 
 needs and preferences of disabled or elderly users who need help with the 
 activities of daily living. However\, the needs and requests of users vary
  greatly\, both due to personal preferences and type of disability. As han
 dcrafting an environment is prohibitively expensive\, in recent years sign
 ificant research was done in systems that use machine learning to create a
  predictive model of the user. Machine learning\, however\, typically requ
 ires large amounts of data. A stand-alone smart environment\, however\, on
 ly has access to the data collected from its user since it was deployed. A
  possible solution is to perform centralized\, cloud-based learning by poo
 ling the training data collected from multiple users. However\, uploading 
 data collected from the personal habits of elderly and disabled users crea
 te significant security and privacy concerns.</p>\n<p>In this talk\, we in
 vestigate the type of data sharing necessary for learning user models in s
 mart environments and propose several novel considerations. We point out t
 hat data sharing is only ethical if the user derives a benefit from it. Th
 is implies that the decision to share data must be periodically revisited\
 , it is not a commitment extending indefinitely in the future. We study th
 e data sharing decisions made by users under several machine learning fram
 eworks: local\, cloud\, and federated learning. We show that most users on
 ly benefit from data sharing for a limited interval after the deployment o
 f the system. We also investigate machine learning techniques that predict
  whether the user will benefit from sharing the data before the data is sh
 ared.</p><br /><br />Agenda: <br /><p>Bio: Dr. Turgut is Charles Millican 
 Professor of Computer Science at the University of Central Florida (UCF). 
 She is the co-director of the AI Things Laboratory. She held visiting rese
 archer positions at the University of Rome ``La Sapienza''\, Imperial Coll
 ege of London\, and KTH Royal Institute of Technology\, Sweden. Her resear
 ch interests include wireless ad hoc\, sensor\, underwater\, vehicular\, a
 nd social networks\, edge/cloud computing\, smart cities\, smart grids\, I
 oT-enabled healthcare and augmented reality\, as well as considerations of
  privacy in the Internet of Things. Dr. Turgut serves on several editorial
  boards and program committees of prestigious ACM and IEEE journals and co
 nferences. Her most recent honors include the NCWIT 2021 Mentoring Award f
 or Undergraduate Research (MAUR)\, the UCF Research Incentive Award\, and 
 the UCF Women of Distinction Award. Since 2019\, she serves as the N2Women
  Board Co-Chair where she co-leads the activities of the N2Women Board in 
 supporting female researchers in the fields of networking and communicatio
 ns. She is an IEEE ComSoc Distinguished Lecturer\, IEEE Senior Member\, an
 d the Chair-Elect of the IEEE Technical Committee on Computer Communicatio
 ns (TCCC).</p>
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