4 papers
PowerScale: Energy-Efficient Geo-Distributed Model Training with Federated Datacenter Power
Talha Mehboob, Zhe Xu, Michael Zink +1
The power demands of large-scale AI training increasingly exceed the capacity of any single data center, making geo-distributed training across power-constrained sites a practical…
AI-Assisted Computational Reproducibility on the FABRIC Testbed
Komal Thareja, Paul Ruth, Berent Aldikacti +1
Computational reproducibility remains difficult despite being central to scientific research. In this paper, we show how the international FABRIC testbed, combined with large langu…
PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training
Talha Mehboob, Luanzheng Guo, Nathan Tallent +2
The exponential growth of large-scale AI models has led to computational and power demands that can exceed the capacity of a single data center. This is due to the limited power su…
EcoLearn: Optimizing the Carbon Footprint of Federated Learning
Talha Mehboob, Noman Bashir, Jesus Omana Iglesias +2
Federated Learning (FL) distributes machine learning (ML) training across edge devices to reduce data transfer overhead and protect data privacy. Since FL model training may span h…