7 citations · 15 across the 10 of their papers we have counts for
7 papers · 1 filter
Variance Reduction for Stochastic Switching in Functionally Constrained Federated Optimization
Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi +1
Federated learning (FL) is increasingly used in safety-critical and fairness-sensitive applications, where learning objectives must be optimized subject to functional constraints.…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Equitable Federated Learning with Activation Clustering
Antesh Upadhyay, Abolfazl Hashemi
Federated learning is a prominent distributed learning paradigm that incorporates collaboration among diverse clients, promotes data locality, and thus ensures privacy. These clien…
Submodular Maximization Approaches for Equitable Client Selection in Federated Learning
Andrés Catalino Castillo Jiménez, Ege C. Kaya, Lintao Ye +1
In a conventional Federated Learning framework, client selection for training typically involves the random sampling of a subset of clients in each iteration. However, this random…
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi +2
To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a globa…
Localized Distributional Robustness in Submodular Multi-Task Subset Selection
Ege C. Kaya, Abolfazl Hashemi
In this work, we treat the problem of multi-task submodular optimization from the perspective of local distributional robustness within the neighborhood of a reference distribution…
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi +1
A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication…