  Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with
distributed intelligence of industrial automation thanks to its capability of
distributed machine learning without any raw data exchange. However, it is
rather challenging for lightweight IIoT devices to perform
computation-intensive local model training over large-scale deep neural
networks (DNNs). Driven by this issue, we develop a communication-computation
efficient FL framework for resource-limited IIoT networks that integrates DNN
partition technique into the standard FL mechanism, wherein IIoT devices
perform local model training over the bottom layers of the objective DNN, and
offload the top layers to the edge gateway side. Considering imbalanced data
distribution, we derive the device-specific participation rate to involve the
devices with better data distribution in more communication rounds. Upon
deriving the device-specific participation rate, we propose to minimize the
training delay under the constraints of device-specific participation rate,
energy consumption and memory usage. To this end, we formulate a joint
optimization problem of device scheduling and resource allocation (i.e. DNN
partition point, channel assignment, transmit power, and computation
frequency), and solve the long-term min-max mixed integer non-linear
programming based on the Lyapunov technique. In particular, the proposed
dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve
a trade-off to balance the training delay minimization and FL performance. We
also provide the FL convergence bound for the DDSRA algorithm with both convex
and non-convex settings. Experimental results demonstrate the derived
device-specific participation rate in terms of feasibility, and show that the
DDSRA algorithm outperforms baselines in terms of test accuracy and convergence
time.
