DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

no code implementations • 3 Jan 2022 • Phillip Rieger, Thien Duc Nguyen, Markus Miettinen, Ahmad-Reza Sadeghi

Using these techniques, DeepSight can identify suspicious model updates.

Federated Learning

BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS

no code implementations • 1 Jan 2021 • Thien Duc Nguyen, Phillip Rieger, Hossein Yalame, Helen Möllering, Hossein Fereidooni, Samuel Marchal, Markus Miettinen, Azalia Mirhoseini, Ahmad-Reza Sadeghi, Thomas Schneider, Shaza Zeitouni

Recently, federated learning (FL) has been subject to both security and privacy attacks posing a dilemmatic challenge on the underlying algorithmic designs: On the one hand, FL is shown to be vulnerable to backdoor attacks that stealthily manipulate the global model output using malicious model updates, and on the other hand, FL is shown vulnerable to inference attacks by a malicious aggregator inferring information about clients’ data from their model updates.

Federated Learning Image Classification

DIoT: A Self-learning System for Detecting Compromised IoT Devices

no code implementations • 20 Apr 2018 • Thien Duc Nguyen, Samuel Marchal, Markus Miettinen, N. Asokan, Ahmad-Reza Sadeghi

Consequently, DIoT can cope with the emergence of new device types as well as new attacks.

Cryptography and Security