The objective of meta-learning is to exploit the knowledge obtained from
observed tasks to improve adaptation to unseen tasks. As such, meta-learners
are able to generalize better when they are trained with a larger number of
observed tasks and with a larger amount of data per task. Given the amount of
resources that are needed, it is generally difficult to expect the tasks, their
respective data, and the necessary computational capacity to be available at a
single central location. It is more natural to encounter situations where these
resources are spread across several agents connected by some graph topology.
The formalism of meta-learning is actually well-suited to this decentralized
setting, where the learner would be able to benefit from information and
computational power spread across the agents. Motivated by this observation, in
this work, we propose a cooperative fully-decentralized multi-agent
meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML.
Decentralized optimization algorithms are superior to centralized
implementations in terms of scalability, avoidance of communication
bottlenecks, and privacy guarantees. The work provides a detailed theoretical
analysis to show that the proposed strategy allows a collection of agents to
attain agreement at a linear rate and to converge to a stationary point of the
aggregate MAML objective even in non-convex environments. Simulation results
illustrate the theoretical findings and the superior performance relative to
the traditional non-cooperative setting