Federated Learning (FL) is a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

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

The minimax problems arise throughout machine learning applications, ranging from
adversarial training and policy evaluation in reinforcement learning to AUROC …

The federated learning (FL) framework enables edge clients to collaboratively learn a
shared inference model while keeping privacy of training data on clients. Recently, many …

The\FedProx~ algorithm is a simple yet powerful distributed proximal point optimization
method widely used for federated learning (FL) over heterogeneous data. Despite its …

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 …

Federated learning (FL) is a promising framework for privacy-preserving and distributed
training with decentralized clients. However, there exists a large divergence between the …

We study the problem of distributed stochastic non-convex optimization with intermittent
communication. We consider the full participation setting where $ M $ machines work in …

Federated min-max learning has received increasing attention in recent years thanks to its
wide range of applications in various learning paradigms. Similar to the conventional …