  We introduce a novel design for in-situ training of machine learning
algorithms built into smart sensors, and illustrate distributed training
scenarios using radio frequency (RF) spectrum sensors. Current RF sensors at
the Edge lack the computational resources to support practical, in-situ
training for intelligent signal classification. We propose a solution using
Deepdelay Loop Reservoir Computing (DLR), a processing architecture that
supports machine learning algorithms on resource-constrained edge-devices by
leveraging delayloop reservoir computing in combination with innovative
hardware. DLR delivers reductions in form factor, hardware complexity and
latency, compared to the State-ofthe- Art (SoA) neural nets. We demonstrate DLR
for two applications: RF Specific Emitter Identification (SEI) and wireless
protocol recognition. DLR enables mobile edge platforms to authenticate and
then track emitters with fast SEI retraining. Once delay loops separate the
data classes, traditionally complex, power-hungry classification models are no
longer needed for the learning process. Yet, even with simple classifiers such
as Ridge Regression (RR), the complexity grows at least quadratically with the
input size. DLR with a RR classifier exceeds the SoA accuracy, while further
reducing power consumption by leveraging the architecture of parallel (split)
loops. To authenticate mobile devices across large regions, DLR can be trained
in a distributed fashion with very little additional processing and a small
communication cost, all while maintaining accuracy. We illustrate how to merge
locally trained DLR classifiers in use cases of interest.
