  Real-time machine learning has recently attracted significant interest due to
its potential to support instantaneous learning, adaptation, and decision
making in a wide range of application domains, including self-driving vehicles,
intelligent transportation, and industry automation. We investigate real-time
ML in a federated edge intelligence (FEI) system, an edge computing system that
implements federated learning (FL) solutions based on data samples collected
and uploaded from decentralized data networks. FEI systems often exhibit
heterogenous communication and computational resource distribution, as well as
non-i.i.d. data samples, resulting in long model training time and inefficient
resource utilization. Motivated by this fact, we propose a time-sensitive
federated learning (TS-FL) framework to minimize the overall run-time for
collaboratively training a shared ML model. Training acceleration solutions for
both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous
coordination (TS-FL-ASC) are investigated. To address straggler effect in
TS-FL-SC, we develop an analytical solution to characterize the impact of
selecting different subsets of edge servers on the overall model training time.
A server dropping-based solution is proposed to allow slow-performance edge
servers to be removed from participating in model training if their impact on
the resulting model accuracy is limited. A joint optimization algorithm is
proposed to minimize the overall time consumption of model training by
selecting participating edge servers, local epoch number. We develop an
analytical expression to characterize the impact of staleness effect of
asynchronous coordination and straggler effect of FL on the time consumption of
TS-FL-ASC. Experimental results show that TS-FL-SC and TS-FL-ASC can provide up
to 63% and 28% of reduction, in the overall model training time, respectively.
