  Federated Learning (FL) provides privacy preservation by allowing the model
training at edge devices without the need of sending the data from edge to a
centralized server. FL has distributed the implementation of ML. Another
variant of FL which is well suited for the Internet of Things (IoT) is known as
Collaborated Federated Learning (CFL), which does not require an edge device to
have a direct link to the model aggregator. Instead, the devices can connect to
the central model aggregator via other devices using them as relays. Although,
FL and CFL protect the privacy of edge devices but raises security challenges
for a centralized server that performs model aggregation. The centralized
server is prone to malfunction, backdoor attacks, model corruption, adversarial
attacks and external attacks. Moreover, edge device to centralized server data
exchange is not required in FL and CFL, but model parameters are sent from the
model aggregator (global model) to edge devices (local model), which is still
prone to cyber-attacks. These security and privacy concerns can be potentially
addressed by Blockchain technology. The blockchain is a decentralized and
consensus-based chain where devices can share consensus ledgers with increased
reliability and security, thus significantly reducing the cyberattacks on an
exchange of information. In this work, we will investigate the efficacy of
blockchain-based decentralized exchange of model parameters and relevant
information among edge devices and from a centralized server to edge devices.
Moreover, we will be conducting the feasibility analysis for blockchain-based
CFL models for different application scenarios like the internet of vehicles,
and the internet of things. The proposed study aims to improve the security,
reliability and privacy preservation by the use of blockchain-powered CFL.
