Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints

Understand

We consider the problem of decentralized optimization where a collection of agents, each having access to a local cost function, communicate over a time-varying directed network and aim to minimize the sum of those functions.

• In practice, the amount of information that can be exchanged between the agents is limited due to communication constraints.
• We propose a communication-efficient algorithm for decentralized convex optimization that rely on sparsification of local updates exchanged between neighboring agents in the network.
• In directed networks, message sparsification alters column-stochasticity -- a property that plays an important role in establishing convergence of decentralized learning tasks.