FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights

TL;DR

FedBChain is a novel blockchain-enabled federated learning framework that enhances DeepConvLSTM performance for human activity recognition while ensuring data privacy and security across distributed datasets.

Contribution

Introduces FedBChain, integrating blockchain with federated DeepConvLSTM, demonstrating improved prediction accuracy and privacy preservation in human activity recognition tasks.

Findings

Significant improvements in Precision, Recall, and F1-score over centralized training.

Federated strategies like FedAvg and FedProx outperform centralized methods by around 4.5%.

Framework guarantees data security and privacy during training.

Abstract

Recent research in the field of Human Activity Recognition has shown that an improvement in prediction performance can be achieved by reducing the number of LSTM layers. However, this kind of enhancement is only significant on monolithic architectures, and when it runs on large-scale distributed training, data security and privacy issues will be reconsidered, and its prediction performance is unknown. In this paper, we introduce a novel framework: FedBChain, which integrates the federated learning paradigm based on a modified DeepConvLSTM architecture with a single LSTM layer. This framework performs comparative tests of prediction performance on three different real-world datasets based on three different hidden layer units (128, 256, and 512) combined with five different federated learning strategies, respectively. The results show that our architecture has significant improvements in Precision, Recall and F1-score compared to the centralized training approach on all datasets with all hidden layer units for all strategies: FedAvg strategy improves on average by 4.54%, FedProx improves on average by 4.57%, FedTrimmedAvg improves on average by 4.35%, Krum improves by 4.18% on average, and FedAvgM improves by 4.46% on average. Based on our results, it can be seen that FedBChain not only improves in performance, but also guarantees the security and privacy of user data compared to centralized training methods during the training process. The code for our experiments is publicly available (https://github.com/Glen909/FedBChain).