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DTSTAMP:20250626T233531Z
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UID:submissions.supercomputing.org_SC24_sess534_post232@linklings.com
SUMMARY:Optimal Client Selection Algorithms for Federated Learning
DESCRIPTION:Alan Nunes (Fluminense Federal University, Brazil; University 
 of Bordeaux, France); Cristina Boeres and Lúcia Drummond (Fluminense Feder
 al University, Brazil); and Laércio Pilla (University of Bordeaux, French 
 Institute for Research in Computer Science and Automation (INRIA))\n\nDue 
 to the heterogeneity of resources and data, client selection plays a param
 ount role in the efficacy of Federated Learning (FL) systems. The time tak
 en by a training round is determined by the slowest client. Also, energy c
 onsumption and carbon footprint are seen as primary concerns. In this cont
 ext, we propose two optimal time- and energy-aware client selection algori
 thms for FL: MEC and ECMTC. To the best of our knowledge, this work is the
  first to propose algorithms that make an optimal selection of clients wit
 h heterogeneous resources by jointly optimizing the execution time and ene
 rgy consumption while defining how much data each client should use locall
 y.\n\nDuring the presentation, I will expose the challenges of selecting c
 lients in FL systems, present our approach based on an illustrative exampl
 e, and then show the experimental evaluation carried out in an HPC platfor
 m and the takeaway of our investigation.\n\nRegistration Category: Tech Pr
 ogram Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedri
 ch-Alexander University, Erlangen-Nuremberg; Erlangen National High Perfor
 mance Computing Center); Sally Ellingson (University of Kentucky); and Ala
 n Sussman (University of Maryland)\n\n
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