Leveraging Transfer Learning Domain Adaptation Model With Federated Learning to Revolutionise Healthcare

Abstract

The application of artificial intelligence (AI) in healthcare has been witnessing an increasing interest. Particularly, federated learning (FL) has become favourable due to its potential for enhancing model quality whilst maintaining data privacy and security. However, the effectiveness of present FL methodologies could underperform under non-IID conditions, characterised by divergent data distributions across clients. The globally constructed FL model may suffer potent issues by allowing the least-performing models to equal participation. Thus, we propose a new accuracy-based FL approach (FedAcc) which only takes into account the clients' validation accuracy to consider their participation during global aggregation, also called Smart Healthcare Amplified (SHA). However, with limited supervised data it is challenging to increase the model performance thus concept of transfer learning (TL) is used. TL enables the global model to integrate knowledge from precomputed systems, resulting in an efficient model. However, the complexity of the global system is amplified by these TL models, leading to challenges related to vanishing gradients, particularly when dealing with a substantial number of layers. To mitigate this, we present a Transfer Learning Domain Adaptation Model (TLDAM). TLDAM employs a two-layered sequentially trained TL model, which contains approximately 50% fewer layers compared to traditional TL models. TLDAM is trained on multiple datasets such as MNIST and CIFAR10, to enhance its knowledge and make it domain-adaptive. Moreover, experimental results conducted on the UCI-HAR dataset reveal the supremacy of our proposed framework with an accuracy of 94.2990%, F-score of 94.2820%, precision of 94.3058%, and recall of 94.2993% over traditional FL techniques and state-of-the-art techniques.