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cs.CR2025
ZORRO: Zero-Knowledge Robustness and Privacy for Split Learning (Full Version)
Nojan Sheybani, Alessandro Pegoraro, Jonathan Knauer +4
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a…
cs.CR2025
SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
Phillip Rieger, Alessandro Pegoraro, Kavita Kumari +3
Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without…
cs.CR2024
Phantom: Untargeted Poisoning Attacks on Semi-Supervised Learning (Full Version)
Jonathan Knauer, Phillip Rieger, Hossein Fereidooni +1
Deep Neural Networks (DNNs) can handle increasingly complex tasks, albeit they require rapidly expanding training datasets. Collecting data from platforms with user-generated conte…