Strategies for Enhancing Training and Privacy in Blockchain Enabled Federated Learning

Abstract

Several recent advances in Federated Learning have made it possible for researchers to train their models on private data present on contributing devices without compromising their privacy. In this paradigm, each contributor’s local updates are aggregated and averaged to update the global model. In this paper, we introduce a secure and decentralized training for distributed data. In order to develop an efficient decentralized system, blockchain technology is introduced via Ethereum, which enables us to create a value-driven incentive mechanism. This is done to encourage the contributors to positively affect the learning of the global model. We provide an enhanced security mechanism by implementing differential privacy and homomorphic encryption. The performance of the global model has been significantly boosted by implementing Elastic Weight Consolidation, which prevents Catastrophic forgetting, a scenario where the model learns only on new data and forgets its previous learnings. It proves essential in distributed training since the model is being trained on a spectrum of data, often present in clusters on each contributor’s device. We introduce an innovative way of using hyperparameter optimization in federated learning with the help of Hyperopt and deposit based reward mechanism. Experiments verify the capability of the novel strategies incorporated in our system.