  Distributed learning, which does not require gathering training data in a
central location, has become increasingly important in the big-data era. In
particular, random-walk-based decentralized algorithms are flexible in that
they do not need a central server trusted by all clients and do not require all
clients to be active in all iterations. However, existing distributed learning
algorithms assume that all learning clients share the same task. In this paper,
we consider the more difficult meta-learning setting, in which different
clients perform different (but related) tasks with limited training data. To
reduce communication cost and allow better privacy protection, we propose
LoDMeta (Local Decentralized Meta-learning) with the use of local auxiliary
optimization parameters and random perturbations on the model parameter.
Theoretical results are provided on both convergence and privacy analysis.
Empirical results on a number of few-shot learning data sets demonstrate that
LoDMeta has similar meta-learning accuracy as centralized meta-learning
algorithms, but does not require gathering data from each client and is able to
better protect data privacy for each client.
