INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks

In recent years, decentralized bilevel optimization problems have received In recent years, fueled by the rise of machine learning and artificial increasing attention in the networking and machine learning intelligence in edge networks, decentralized bilevel optimization communities thanks to their versatility in modeling decentralized problems have received increasing attention in the networking and learning problems over peer-to-peer networks (e.g., multi-agent machine learning communities. This is due to the versatility of meta-learning, multi-agent reinforcement learning, personalized decentralized bilevel optimization in supporting many decentralized training, and Byzantine-resilient learning). However, for decentralized learning paradigms over peer-to-peer networks, such as the bilevel optimization over peer-to-peer networks with limited multi-agent versions of meta learning [22, 33, 33], hyperparameter computation and communication capabilities, how to achieve low optimization problem[24, 29], area under curve (AUC) problems sample and communication complexities are two fundamental challenges [19, 32], and reinforcement learning[9, 40]. To date, however, that remain under-explored so far. In this paper, we make there remain many challenges and open problems in decentralized the first attempt to investigate the class of decentralized bilevel bilevel learning over peer-to-peer networks. Two of the most optimization problems with nonconvex and strongly-convex structure fundamental challenges in decentralized bilevel optimization are corresponding to the outer and inner subproblems, respectively.