Abstract
Recent advancements in Deep Neural Networks (DNNs) have catalyzed the
development of numerous intelligent mobile applications and services. However,
they also introduce significant computational challenges for
resource-constrained mobile devices. To address this, collaborative edge
inference has been proposed. This method involves partitioning a DNN inference
task into several subtasks and distributing these across multiple network
nodes. Despite its potential, most current approaches presume known network
parameters -- like node processing speeds and link transmission rates -- or
rely on a fixed sequence of nodes for processing the DNN subtasks. In this
paper, we tackle a more complex scenario where network parameters are unknown
and must be learned, and multiple network paths are available for distributing
inference tasks. Specifically, we explore the learning problem of selecting the
optimal network path and assigning DNN layers to nodes along this path,
considering potential security threats and the costs of switching paths. We
begin by deriving structural insights from the DNN layer assignment with
complete network information, which narrows down the decision space and
provides crucial understanding of optimal assignments. We then cast the
learning problem with incomplete network information as a novel adversarial
group linear bandits problem with switching costs, featuring rewards generation
through a combined stochastic and adversarial process. We introduce a new
bandit algorithm, B-EXPUCB, which combines elements of the classical blocked
EXP3 and LinUCB algorithms, and demonstrate its sublinear regret. Extensive
simulations confirm B-EXPUCB's superior performance in learning for
collaborative edge inference over existing algorithms.