Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks
This work addresses security vulnerabilities in distributed collaborative machine learning, specifically for privacy-sensitive applications like healthcare and digit recognition, and is incremental as it builds on existing hybrid approaches.
The study investigated the robustness of SplitFed Learning against data poisoning attacks by proposing three novel attack strategies and testing them on electrocardiogram signal classification and handwritten digit recognition tasks, finding that untargeted and distance-based attacks significantly degrade classifier performance more than targeted attacks.
Distributed Collaborative Machine Learning (DCML) is a potential alternative to address the privacy concerns associated with centralized machine learning. The Split learning (SL) and Federated Learning (FL) are the two effective learning approaches in DCML. Recently there have been an increased interest on the hybrid of FL and SL known as the SplitFed Learning (SFL). This research is the earliest attempt to study, analyze and present the impact of data poisoning attacks in SFL. We propose three kinds of novel attack strategies namely untargeted, targeted and distance-based attacks for SFL. All the attacks strategies aim to degrade the performance of the DCML-based classifier. We test the proposed attack strategies for two different case studies on Electrocardiogram signal classification and automatic handwritten digit recognition. A series of attack experiments were conducted by varying the percentage of malicious clients and the choice of the model split layer between the clients and the server. The results after the comprehensive analysis of attack strategies clearly convey that untargeted and distance-based poisoning attacks have greater impacts in evading the classifier outcomes compared to targeted attacks in SFL