Key result
Server-less federated learning detects ECG cardiac irregularities using a dual-way update approach.
Why the study?
Data privacy regulations, data handling concerns, and growing data sizes have motivated the need for robust, decentralized federated learning approaches resilient to Byzantine attacks.
A novel server-less, robust federated learning mechanism demonstrates efficiency in detecting cardiac irregularities from ECG data while remaining resilient to malicious updates.
Advances server-less federated ECG analysis; leaves open prospective clinical validation before adoption.
Federated Learning (FL) has attracted a lot of attention in numerous applications due to recent data privacy regulations and increased awareness about data handling issues, combined with the ever-increasing big-data sizes. This paper proposes a server-less, robust FL training mechanism that allows any set of participating data-owners to train a neural network (NN) model collaboratively without the assistance of any central node and while being resilient to Byzantine attacks. The proposed approach makes use of a dual-way update mechanism to allow each node to take a model forwarding decision towards a global collaborative decision of isolating any malicious updates. The efficiency of the proposed approach in detecting cardiac irregularities is verified using simulation results conducted based on the Physikalisch-Technische Bundesanstalt Database electro-cardiogram (PTBDB ECG) dataset.
No takes yet. Share an insight, caveat, or question.
Gouissem et al. (2022) studied Cardiac irregularities. Server-less, robust Federated Learning training mechanism was evaluated on Detection of cardiac irregularities. A server-less, robust federated learning mechanism using a dual-way update approach was proposed and verified for detecting cardiac irregularities using the PTBDB ECG dataset.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: