Federated learning has attracted increasing attention with the emergence of distributed data.
While extensive federated learning algorithms have been proposed for the non-convex …

We develop and analyze MARINA: a new communication efficient method for non-convex
distributed learning over heterogeneous datasets. MARINA employs a novel communication …

Decentralized training of deep learning models is a key element for enabling data privacy
and on-device learning over networks. In realistic learning scenarios, the presence of …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Conditional stochastic optimization has found applications in a wide range of machine
learning tasks, such as invariant learning, AUPRC maximization, and meta-learning. As the …

Data-heterogeneous federated learning (FL) systems suffer from two significant sources of
convergence error: 1) client drift error caused by performing multiple local optimization steps …

In federated learning (FL), a cluster of local clients are chaired under the coordination of the
global server and cooperatively train one model with privacy protection. Due to the multiple …

Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a
joint model by using local data. Despite extensive research, for a generic non-convex FL …

Existing theory predicts that data heterogeneity will degrade the performance of the
Federated Averaging (FedAvg) algorithm in federated learning. However, in practice, the …

Recently, local SGD has got much attention and been extensively studied in the distributed
learning community to overcome the communication bottleneck problem. However, the …