Virat Shejwalkar (UMass Amherst), Amir Houmansadr (UMass Amherst)

Federated learning (FL) enables many data owners (e.g., mobile devices) to train a joint ML model (e.g., a next-word prediction classifier) without the need of sharing their private training data.

However, FL is known to be susceptible to poisoning attacks by malicious participants (e.g., adversary-owned mobile devices) who aim at hampering the accuracy of the jointly trained model through sending malicious inputs during the federated training process.

In this paper, we present a generic framework for model poisoning attacks on FL. We show that our framework leads to poisoning attacks that substantially outperform state-of-the-art model poisoning attacks by large margins. For instance, our attacks result in $1.5times$ to $60times$ higher reductions in the accuracy of FL models compared to previously discovered poisoning attacks.

Our work demonstrates that existing Byzantine-robust FL algorithms are significantly more susceptible to model poisoning than previously thought. Motivated by this, we design a defense against FL poisoning, called emph{divide-and-conquer} (DnC). We demonstrate that DnC outperforms all existing Byzantine-robust FL algorithms in defeating model poisoning attacks,
specifically, it is $2.5times$ to $12times$ more resilient in our experiments with different datasets and models.

View More Papers

PGFUZZ: Policy-Guided Fuzzing for Robotic Vehicles

Hyungsub Kim (Purdue University), Muslum Ozgur Ozmen (Purdue University), Antonio Bianchi (Purdue University), Z. Berkay Celik (Purdue University), Dongyan Xu (Purdue University)

Read More

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

Read More

Obfuscated Access and Search Patterns in Searchable Encryption

Zhiwei Shang (University of Waterloo), Simon Oya (University of Waterloo), Andreas Peter (University of Twente), Florian Kerschbaum (University of Waterloo)

Read More

The Nuts and Bolts of Building FlowLens

Diogo Barradas (Instituto Superior Técnico, Universidade de Lisboa)

Read More