Privacy Integrity-Aware Blockchain Communication in Federated Edge Learning Platform

Abstract

The blockchain architecture offers transparent security mechanisms in a decentralized manner; due to this, it has attained increasing growth in a federated edge-server learning environment. In federated learning, the data model is executed in multiple edge servers in a collaborative manner, increasing users’ privacy and data-integrity breach because of single point failure attack in the main computational server. Blockchain employing a rewarding mechanism in federated edge-learning platform aids the model to overcome single-point aggregation failure. However, the current method failed to identify selfish and baized workers; further, reaching global consensus model to assure privacy-integrity in blockchain-enabled federated edge-server is difficult. This paper presents privacy-integrity-aware blockchain communication (PIABC) in federated edge-server learning platform. The PIABC model is very effective in comparison with existing blockchain-privacy preserving schemes for identifying the correctly aggregated packets and eliminating malicious packets within the federated edge-server learning platform.