SecureSplit: Mitigating Backdoor Attacks in Split Learning

Split Learning SL offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset while maintaining distinct feature sets. However, SL is susceptible to backdoor attacks, in which malicious clients subtly alter their embeddings to...

A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing Constraints

Federated Learning has gained increasing attention for its ability to enable multiple nodes to collaboratively train machine learning models without sharing their raw data. At the same time, Generative AI -- particularly Generative Adversarial Networks GANs -- have achieved remarkable success...

Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

Split Learning SL -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning ML processes. Though promising, SL has proven vulnerable to different attacks, thus raising concerns about how effective it may be in terms of data privacy. Recent works have...

A Taxonomy of Attacks and Defenses in Split Learning

Split Learning SL has emerged as a promising paradigm for distributed deep learning, allowing resource-constrained clients to offload portions of their model computation to servers while maintaining collaborative learning. However, recent research has demonstrated that SL remains vulnerable to a...