3 papers
cs.DC2026
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…
cs.DC2025
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…
cs.LG2025
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…