27 citations · 43 across the 8 of their papers we have counts for
7 papers · 1 filter
Adaptive Determinantal Client Scheduling in Federated Learning
Wen Xu, Ben Liang, Gary Boudreau +1
Scheduling clients for model training is critical in federated learning due to both data and system heterogeneity. Most previous works focus on the quality of the scheduled clients…
Adaptive Data Admission and Retention for Streaming Federated Learning
Zhuoyi Zhao, Ben Liang
We study streaming federated learning with limited client memory, where newly generated training data incur time-varying sampling costs and must be selectively admitted and retaine…
Novel Gradient Sparsification Algorithm via Bayesian Inference
Ali Bereyhi, Ben Liang, Gary Boudreau +1
Error accumulation is an essential component of the Top- sparsification method in distributed gradient descent. It implicitly scales the learning rate and prevents the slow-down…
Generative Adversarial Classification Network with Application to Network Traffic Classification
Rozhina Ghanavi, Ben Liang, Ali Tizghadam
Large datasets in machine learning often contain missing data, which necessitates the imputation of missing data values. In this work, we are motivated by network traffic classific…
Dynamic Regret of Online Mirror Descent for Relatively Smooth Convex Cost Functions
Nima Eshraghi, Ben Liang
The performance of online convex optimization algorithms in a dynamic environment is often expressed in terms of the dynamic regret, which measures the decision maker's performance…
On the Generalization of Stochastic Gradient Descent with Momentum
Ali Ramezani-Kebrya, Ashish Khisti, Ben Liang
While momentum-based methods, in conjunction with stochastic gradient descent (SGD), are widely used when training machine learning models, there is little theoretical understandin…