To address the big data challenges, serverless multi-party collaborative training has recently
attracted attention in the data mining community, since they can cut down 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 …

Decentralized bilevel optimization has been actively studied in the past few years since it
has widespread applications in machine learning. However, existing algorithms suffer from …

Stochastic bilevel optimization (SBO) is becoming increasingly essential in machine
learning due to its versatility in handling nested structures. To address large-scale SBO …

Decentralized learning has emerged as an alternative method to the popular parameter-
server framework which suffers from high communication burden, single-point failure and …

Decentralized bilevel optimization has received increasing attention recently due to its
foundational role in many emerging multi-agent learning paradigms (eg, multi-agent meta …

While personalization in distributed learning has been extensively studied, existing
approaches employ dedicated algorithms to optimize their specific type of parameters (eg …

This paper addresses the communication issues when estimating hyper-gradients in
decentralized federated learning (FL). Hyper-gradients in decentralized FL quantifies how …

This research can be divided into two aspects: the development of a deductive failure
analysis method for Backwards Logic Inference based Propagation for System analysis …

In recent years, constrained decentralized stochastic bilevel optimization has become
increasingly important due to its versatility in modeling a wide range of multi-agent learning …