1 citations · 1 across the 10 of their papers we have counts for
10 papers
FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
Harekrushna Sahu, Pratik Jawanpuria, Pranay Sharma
Online federated learning (OFL) has emerged as a popular framework for decentralized decision-making over continuous data streams without compromising client privacy. However, the…
Natural Policy Gradient for Average Reward Non-Stationary RL
Neharika Jali, Eshika Pathak, Pranay Sharma +2
We consider the problem of non-stationary reinforcement learning (RL) in the infinite-horizon average-reward setting. We model it by a Markov Decision Process with time-varying rew…
Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning
Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu +1
Initializing with pre-trained models when learning on downstream tasks is becoming standard practice in machine learning. Several recent works explore the benefits of pre-trained i…
Improving the Convergence of Private Shuffled Gradient Methods with Public Data
Shuli Jiang, Pranay Sharma, Zhiwei Steven Wu +1
We consider the problem of differentially private (DP) convex empirical risk minimization (ERM). While the standard DP-SGD algorithm is theoretically well-established, practical im…
Debiasing Federated Learning with Correlated Client Participation
Zhenyu Sun, Ziyang Zhang, Zheng Xu +3
In cross-device federated learning (FL) with millions of mobile clients, only a small subset of clients participate in training in every communication round, and Federated Averagin…
Federated Communication-Efficient Multi-Objective Optimization
Baris Askin, Pranay Sharma, Gauri Joshi +1
We study a federated version of multi-objective optimization (MOO), where a single model is trained to optimize multiple objective functions. MOO has been extensively studied in th…
Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
Aleksandar Armacki, Shuhua Yu, Pranay Sharma +4
We study high-probability convergence in online learning, in the presence of heavy-tailed noise. To combat the heavy tails, a general framework of nonlinear SGD methods is consider…
FedAST: Federated Asynchronous Simultaneous Training
Baris Askin, Pranay Sharma, Carlee Joe-Wong +1
Federated Learning (FL) enables edge devices or clients to collaboratively train machine learning (ML) models without sharing their private data. Much of the existing work in FL fo…
Correlation Aware Sparsified Mean Estimation Using Random Projection
Shuli Jiang, Pranay Sharma, Gauri Joshi
We study the problem of communication-efficient distributed vector mean estimation, a commonly used subroutine in distributed optimization and Federated Learning (FL). Rand- spa…
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
Aleksandar Armacki, Pranay Sharma, Gauri Joshi +3
We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise. In the proposed scenario, the model is updated in an online fa…