INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks. (arXiv:2207.13283v3 [cs.LG] UPDATED)

In recent years, decentralized bilevel optimization problems have received
increasing attention in the networking and machine learning communities thanks
to their versatility in modeling decentralized learning problems over
peer-to-peer networks (e.g., multi-agent meta-learning, multi-agent
reinforcement learning, personalized training, and Byzantine-resilient
learning). However, for decentralized bilevel optimization over peer-to-peer
networks with limited computation and communication capabilities, how to
achieve low sample and communication complexities are two fundamental
challenges that remain under-explored so far. In this paper, we make the first
attempt …

arxiv communication decentralized low networks