575 citations · 697 across the 8 of their papers we have counts for
8 papers
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies
Yae Jee Cho, Jianyu Wang, Gauri Joshi
Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several…
Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning
Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura +2
While Deep Neural Networks (DNNs) achieve state-of-the-art accuracy in various applications, they often fall short in accurately estimating their predictive uncertainty and, in tur…
Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
Jianyu Wang, Qinghua Liu, Hao Liang +2
In federated optimization, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client i…
Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD
Jianyu Wang, Hao Liang, Gauri Joshi
Distributed stochastic gradient descent (SGD) is essential for scaling the machine learning algorithms to a large number of computing nodes. However, the infrastructures variabilit…
Accelerating Deep Learning by Focusing on the Biggest Losers
Angela H. Jiang, Daniel L. -K. Wong, Giulio Zhou +8
This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration. Select…
Throughput-Smoothness Trade-offs in Multicasting of an Ordered Packet Stream
Gauri Joshi, Yuval Kochman, Gregory Wornell
An increasing number of streaming applications need packets to be strictly in-order at the receiver. This paper provides a framework for analyzing in-order packet delivery in such…