Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

We introduce a novel federated learning framework allowing multiple parties having different
sets of attributes about the same user to jointly build models without exposing their raw data …

The scale of modern datasets necessitates the development of efficient distributed
optimization methods for machine learning. We present a general-purpose framework for …

We introduce a collaborative learning framework allowing multiple parties having different
sets of attributes about the same user to jointly build models without exposing their raw data …

Most of the existing FL systems focus on a data-parallel architecture where training data are
partitioned by samples among several parties. In some real-life applications, however …

Deep learning has aroused extensive attention due to its great empirical success. The
efficiency of the block coordinate descent (BCD) methods has been recently demonstrated …

We consider online no-regret learning in unknown games with bandit feedback, where each
player can only observe its reward at each time—determined by all players' current joint …

This paper introduces AdaSDCA: an adaptive variant of stochastic dual coordinate ascent
(SDCA) for solving the regularized empirical risk minimization problems. Our modification …

Although Federated Learning has been widely studied in recent years, there are still high
overhead expenses in each communication round for large-scale models such as Vision …

Due to the rapid growth of data and computational resources, distributed optimization has
become an active research area in recent years. While first-order methods seem to dominate …