Blockchain-based Collaborated Federated Learning for Improved Security, Privacy and Reliability [article]

Amir Afaq, Zeeshan Ahmed, Noman Haider, Muhammad Imran
2022 arXiv   pre-print
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
more » ... ther 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.
arXiv:2201.08551v1 fatcat:urdgdb2nerhxhjoynrkhnryqem