@techreport{TR-IC-PFG-22-04,
   number = {IC-PFG-22-04},
   author  = {Renata Mancilha Lellis and Luiz Fernando Bittencourt and
                   Júlio César Santos dos Anjos},
   title  =  {{Entropia  como  otimizador  no  aprendizado  de máquina
                   federado: Um Estudo de Caso}},
   month = {Junho},
   year = {2022},
   institution = {Institute of Computing, University of Campinas},
   note = {In Portuguese, 50 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Machine  learning  has an increasing importance within society.
       However,  for  it  to  continue  to  evolve, more data from IoT
       devices  and computing resources are needed, which has become a
       bottleneck  for  the  scalability  of  this technology. \par In
       order  to seek alternatives to solve the scarcity of resources,
       the   option   of   migrating  the  original  machine  learning 
       configuration  in which there is a central server that receives
       and  stores  all  data from edge devices and builds a model has
       been  studied.  A  federated architecture, known as distributed
       machine learning, performs the distribution of decision making,
       in  a  way  that  does  not  require  the sharing of user data,
       instead,  it  shares  its  learning  model and receives trained
       local  models  and aggregates them. \par However, this proposal
       still  finds  some  relevant  points  that should be considered
       limiting.  Thus,  this  project  aims to show a case study of a
       form  of  optimization  in  the  selection  of  users  who will
       participate   in   the   model  training,  in  order  to  avoid 
       unnecessary  processing expenses and to verify if this proposed
       way is capable of bringing dif erences that are significant for
       applications of federated learning on IoT devices.
  }
}