arXiv subjects

Talha Mehboob

Publications and source records attributed to Talha Mehboob.

3 recordsLinked to original sources

arXiv2026

PowerScale: Energy-Efficient Geo-Distributed Model Training with Federated Datacenter Power

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 necessity. Prior work optimizes such training mainly for time-to-accuracy using single-tier aggregation, where every site exchanges model updates directly with a central aggregator over the WAN each synchronization round, without accounting for the energy required to reach convergence. Single-tier aggregation is fundamentally energy-inefficient because synchronization barriers force faster sites to idle, full WAN updates dominate communication energy at scale, and fixed synchronization frequency keeps paying the same communication cost even when updates shrink late in training. To address these inefficiencies, we present PowerScale, a hierarchical aggregation system that exploits the latency hierarchy of wide-area networks. PowerScale organizes sites into regional clusters and applies a Sync-Async synchronization modality: sites synchronize frequently with a nearby cluster aggregator over fast local links, while cluster aggregators push pre-aggregated updates asynchronously to a global aggregator over the WAN. PowerScale forms clusters based on both network proximity and power availability, and uses an adaptive synchronization policy that reduces communication energy by adjusting how often clusters synchronize to training progress. This structure shortens synchronization barriers and replaces per-site WAN transmissions with fewer, pre-aggregated transmissions at a lower frequency. We evaluate PowerScale at 100-site scale in a Flower-based simulation environment. PowerScale matches or slightly improves time-to-accuracy compared with single-tier baselines while reducing energy consumption by up to 3.9x.

cs.DC↗

arXiv2025

PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training

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 supplied by regional grids that leads to limited regional computational power. Consequently, distributing training workloads across geographically distributed sites has become essential. However, this approach introduces a significant challenge in the form of communication overhead, creating a fundamental trade-off between the performance gains from accessing greater aggregate power and the performance losses from increased network latency. Although prior work has focused on reducing communication volume or using heuristics for distribution, these methods assume constant homogeneous power supplies and ignore the challenge of heterogeneous power availability between sites. To address the challenge of training large models in power-constrained, geo-distributed environments, we introduce PowerTrip, a system that dynamically selects a subset of sites during runtime to optimize the power-communication trade-off. Specifically, PowerTrip selects sites based on a power-to-cost heuristic, prioritizing those with high power availability and low network latency. PowerTrip employs a dynamic greedy approach and uses the marginal gain in training efficiency, i.e., accuracy improvement per unit of time, to optimize for the number of sites where the performance penalty from network overhead negates the benefit of adding more computational power. Our evaluation, which uses real-world Google power traces to model realistic power capacity constraints, demonstrates that PowerTrip can reduce time-to-accuracy by up to 50% compared to existing baseline policies.

cs.DC↗

arXiv2023

EcoLearn: Optimizing the Carbon Footprint of Federated Learning

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 hundreds of devices and is thus resource- and energy-intensive, it has a significant carbon footprint. Importantly, since energy's carbon-intensity differs substantially (by up to 60$\times$) across locations, training on the same device using the same amount of energy, but at different locations, can incur widely different carbon emissions. While prior work has focused on improving FL's resource- and energy-efficiency by optimizing time-to-accuracy, it implicitly assumes all energy has the same carbon intensity and thus does not optimize carbon efficiency, i.e., work done per unit of carbon emitted. To address the problem, we design EcoLearn, which minimizes FL's carbon footprint without significantly affecting model accuracy or training time. EcoLearn achieves a favorable tradeoff by integrating carbon awareness into multiple aspects of FL training, including i) selecting clients with high data utility and low carbon, ii) provisioning more clients during the initial training rounds, and iii) mitigating stragglers by dynamically adjusting client over-provisioning based on carbon. We implement EcoLearn and its carbon-aware FL training policies in the Flower framework and show that it reduces the carbon footprint of training (by up to $10.8$$\times$) while maintaining model accuracy and training time (within $\sim$$1$\%) compared to state-of-the-art approaches.

cs.LG↗