  Distributed machine learning (ML) at network edge is a promising paradigm
that can preserve both network bandwidth and privacy of data providers.
However, heterogeneous and limited computation and communication resources on
edge servers (or edges) pose great challenges on distributed ML and formulate a
new paradigm of Edge Learning (i.e. edge-cloud collaborative machine learning).
In this article, we propose a novel framework of 'learning to learn' for
effective Edge Learning (EL) on heterogeneous edges with resource constraints.
We first model the dynamic determination of collaboration strategy (i.e. the
allocation of local iterations at edge servers and global aggregations on the
Cloud during collaborative learning process) as an online optimization problem
to achieve the tradeoff between the performance of EL and the resource
consumption of edge servers. Then, we propose an Online Learning for EL (OL4EL)
framework based on the budget-limited multi-armed bandit model. OL4EL supports
both synchronous and asynchronous learning patterns, and can be used for both
supervised and unsupervised learning tasks. To evaluate the performance of
OL4EL, we conducted both real-world testbed experiments and extensive
simulations based on docker containers, where both Support Vector Machine and
K-means were considered as use cases. Experimental results demonstrate that
OL4EL significantly outperforms state-of-the-art EL and other collaborative ML
approaches in terms of the trade-off between learning performance and resource
consumption.
