  Over the years, the popularity and usage of wearable Internet of Things (IoT)
devices in several healthcare services are increased. Among the services that
benefit from the usage of such devices is predictive analysis, which can
improve early diagnosis in e-health. However, due to the limitations of
wearable IoT devices, challenges in data privacy, service integrity, and
network structure adaptability arose. To address these concerns, we propose a
platform using federated learning and private blockchain technology within a
fog-IoT network. These technologies have privacy-preserving features securing
data within the network. We utilized the fog-IoT network's distributive
structure to create an adaptive network for wearable IoT devices. We designed a
testbed to examine the proposed platform's ability to preserve the integrity of
a classifier. According to experimental results, the introduced implementation
can effectively preserve a patient's privacy and a predictive service's
integrity. We further investigated the contributions of other technologies to
the security and adaptability of the IoT network. Overall, we proved the
feasibility of our platform in addressing significant security and privacy
challenges of wearable IoT devices in predictive healthcare through analysis,
simulation, and experimentation.
