Training algorithms for large language models are often communication heavy. As a result, these models are trained dominantly in a centralized environment such as data centers with fast network connections. This strong dependency on fast interconnections is becoming the limiting factor of further scaling for the data center setting and alternative decentralized infrastructures such as spot instances and geo-distributed volunteer computes. In this talk, I will discuss our research in communication-efficient distributed learning and our current effort in training foundation models in a decentralized way.