1 citations · 1 across the 6 of their papers we have counts for
6 papers
FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
Yijiang Li, Emon Dey, Zilinghan Li +3
Federated learning (FL) across multiple HPC facilities faces stochastic admission delays from batch schedulers that dominate wall-clock time. Synchronous FL suffers from severe str…
Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation
Fateme Maleki, Krishnan Raghavan, Farzad Yousefian
Federated learning (FL) is a communication-efficient collaborative learning framework that enables model training across multiple agents with private local datasets. While the bene…
On Understanding of the Dynamics of Model Capacity in Continual Learning
Supriyo Chakraborty, Krishnan Raghavan
The stability-plasticity dilemma, closely related to a neural network's (NN) capacity-its ability to represent tasks-is a fundamental challenge in continual learning (CL). Within t…
Sampling Imbalanced Data with Multi-objective Bilevel Optimization
Karen Medlin, Sven Leyffer, Krishnan Raghavan
Two-class classification problems are often characterized by an imbalance between the number of majority and minority datapoints resulting in poor classification of the minority cl…
DGRO: Diameter-Guided Ring Optimization for Integrated Research Infrastructure Membership
Shixun Wu, Krishnan Raghavan, Sheng Di +2
Logical ring is a core component in membership protocol. However, the logic ring fails to consider the underlying physical latency, resulting in a high diameter. To address this is…
A Bilevel Optimization Framework for Imbalanced Data Classification
Karen Medlin, Sven Leyffer, Krishnan Raghavan
Data rebalancing techniques, including oversampling and undersampling, are a common approach to addressing the challenges of imbalanced data. To tackle unresolved problems related…