Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification

Deep learning-based medical image analysis is an effective and precise method for identifying various cancer types. However, due to concerns over patient privacy, sharing diagnostic images across medical facilities is typically not permitted. Federated learning (FL) tries to construct a shared model across dispersed clients under such privacy-preserving constraints. Although there is a good chance of success, dealing with non-IID (non-independent and identical distribution) client data, which is a typical circumstance in real-world FL tasks, is still difficult for FL. We use two FL algorithms, FedAvg and FedProx, to manage client heterogeneity and non-IID data in a federated setting. A heterogeneous data split of the cancer datasets with three different forms of cancer—cervical, lung, and colon—is used to validate the efficacy of the FL. In addition, since hyperparameter optimization presents new difficulties in an FL setting, we also examine the impact of various hyperparameter values. We use Bayesian optimization to fine-tune the hyperparameters and identify the appropriate values in order to increase performance. Furthermore, we investigate the hyperparameter optimization in both local and global models of the FL environment. Through a series of experiments, we find that FedProx outperforms FedAvg in scenarios with significant levels of heterogeneity.