188 citations · 201 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning
Hossein Fereidooni, Alessandro Pegoraro, Phillip Rieger +2
Federated learning (FL) is a collaborative learning paradigm allowing multiple clients to jointly train a model without sharing their training data. However, FL is susceptible to p…
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks
Jorge Castillo, Phillip Rieger, Hossein Fereidooni +2
Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning…
FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning
Huimin Li, Phillip Rieger, Shaza Zeitouni +2
Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be…