575 citations · 1k across the 18 of their papers we have counts for
27 papers
Tackling Heterogeneous Traffic in Multi-access Systems via Erasure Coded Servers
Tuhinangshu Choudhury, Weina Wang, Gauri Joshi
Most data generated by modern applications is stored in the cloud, and there is an exponential growth in the volume of jobs to access these data and perform computations using them…
Job Dispatching Policies for Queueing Systems with Unknown Service Rates
Tuhinangshu Choudhury, Gauri Joshi, Weina Wang +1
In multi-server queueing systems where there is no central queue holding all incoming jobs, job dispatching policies are used to assign incoming jobs to the queue at one of the ser…
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…
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…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…