Presentation
Optimal Client Selection Algorithms for Federated Learning
DescriptionDue to the heterogeneity of resources and data, client selection plays a paramount role in the efficacy of Federated Learning (FL) systems. The time taken by a training round is determined by the slowest client. Also, energy consumption and carbon footprint are seen as primary concerns. In this context, we propose two optimal time- and energy-aware client selection algorithms 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 with heterogeneous resources by jointly optimizing the execution time and energy consumption while defining how much data each client should use locally.
During the presentation, I will expose the challenges of selecting clients in FL systems, present our approach based on an illustrative example, and then show the experimental evaluation carried out in an HPC platform and the takeaway of our investigation.
During the presentation, I will expose the challenges of selecting clients in FL systems, present our approach based on an illustrative example, and then show the experimental evaluation carried out in an HPC platform and the takeaway of our investigation.

Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX
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