  With Machine Learning (ML) services now used in a number of mission-critical
human-facing domains, ensuring the integrity and trustworthiness of ML models
becomes all-important. In this work, we consider the paradigm where cloud
service providers collect big data from resource-constrained devices for
building ML-based prediction models that are then sent back to be run locally
on the intermittently-connected resource-constrained devices. Our proposed
solution comprises an intelligent polynomial-time heuristic that maximizes the
level of trust of ML models by selecting and switching between a subset of the
ML models from a superset of models in order to maximize the trustworthiness
while respecting the given reconfiguration budget/rate and reducing the cloud
communication overhead. We evaluate the performance of our proposed heuristic
using two case studies. First, we consider Industrial IoT (IIoT) services, and
as a proxy for this setting, we use the turbofan engine degradation simulation
dataset to predict the remaining useful life of an engine. Our results in this
setting show that the trust level of the selected models is 0.49% to 3.17% less
compared to the results obtained using Integer Linear Programming (ILP).
Second, we consider Smart Cities services, and as a proxy of this setting, we
use an experimental transportation dataset to predict the number of cars. Our
results show that the selected model's trust level is 0.7% to 2.53% less
compared to the results obtained using ILP. We also show that our proposed
heuristic achieves an optimal competitive ratio in a polynomial-time
approximation scheme for the problem.
