Blockchain-Enhanced Federated Learning for Privacy-Preserving Model Training in Cloud Computing Systems

Abstract

Federated Learning (FL) lets multiple devices work together to train a model while keeping data private. However, it has problems with managing trust, keeping the model’s integrity, and finding malicious participants in cloud environments. This article suggests TrustFLChain, a block-chain based FL framework that combines a decentralized trust management system with a scheme for collecting models based on deep learning. TrustFLChain uses a consortium blockchain to make sure that model updates can’t be changed, that participants’ trust is evaluated in a strong way using a hybrid identity-behavior trust model, and that the Proof-of-Trust (PoT) consensus mechanism works well. Trust scoring and the fraudulent detection updates are both automated by the use of smart contracts. Testing done on variety of datasets like the CIFAR-10, MNIST, and ImageNet shows that TrustFLChain is 6.5% more accurate, 38% faster to identify harmful updates, 28% faster at reaching a consensus, and 22% less energy-hungry than the current best methods. Significant improvements are supported by statistical studies (p < 0.01). TrustFLChain provides a flexible, safe, and private way to use trusted AI in cloud systems, perfect for a wide range of uses.