Switching to Learn
This addresses communication efficiency in distributed learning for multi-agent systems, but it appears incremental as it builds on existing Bayesian and non-Bayesian approaches.
The paper tackles the problem of agents learning an unknown state from private signals by proposing a switching method between Bayesian and non-Bayesian regimes to reduce communication costs, achieving efficient learning with fewer communication rounds as verified by simulations.
A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set. Agents observe private signals conditioned on the true state, and form beliefs about the unknown state accordingly. Each agent may face an identification problem in the sense that she cannot distinguish the truth in isolation. However, by communicating with each other, agents are able to benefit from side observations to learn the truth collectively. Unlike many distributed algorithms which rely on all-time communication protocols, we propose an efficient method by switching between Bayesian and non-Bayesian regimes. In this model, agents exchange information only when their private signals are not informative enough; thence, by switching between the two regimes, agents efficiently learn the truth using only a few rounds of communications. The proposed algorithm preserves learnability while incurring a lower communication cost. We also verify our theoretical findings by simulation examples.