Bilevel optimization recently has received tremendous attention due to its great success in
solving important machine learning problems like meta learning, reinforcement learning …

Federated bilevel optimization (FBO) has shown great potential recently in machine learning
and edge computing due to the emerging nested optimization structure in meta-learning …

Bilevel optimization, which captures the inherent nested structure of machine learning
problems, is gaining popularity in many recent applications. Existing works on bilevel …

Federated bilevel learning has received increasing attention in various emerging machine
learning and communication applications. Recently, several Hessian-vector-based …

Bilevel Optimization has witnessed notable progress recently with new emerging efficient
algorithms. However, its application in the Federated Learning setting remains relatively …

The growing size of available data has attracted increasing interest in solving minimax
problems in a decentralized manner for various machine learning tasks. Previous theoretical …

Federated bilevel optimization has attracted increasing attention due to emerging machine
learning and communication applications. The biggest challenge lies in computing the …

Bilevel optimization is one of the fundamental problems in machine learning and
optimization. Recent theoretical developments in bilevel optimization focus on finding the …

In recent years, decentralized bilevel optimization has gained significant attention thanks to
its versatility in modeling a wide range of multi-agent learning problems, such as multi-agent …

We propose a novel single-loop decentralized algorithm, DGDA-VR, for solving the
stochastic nonconvex strongly-concave minimax problems over a connected network of …