Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collaboratively. In this work, we propose FedDefender, a defense mechanism against targeted poisoning attacks in FL by leveraging differential testing. Our proposed method fingerprints the neuron activations of clients' models on the same input and uses differential testing to identify a potentially malicious client containing a backdoor. We evaluate FedDefender using MNIST and FashionMNIST datasets with 20 and 30 clients, and our results demonstrate that FedDefender effectively mitigates such attacks, reducing the attack success rate (ASR) to 10\% without deteriorating the global model performance.
@article{arxiv.2307.08672,
title = {FedDefender: Backdoor Attack Defense in Federated Learning},
author = {Waris Gill and Ali Anwar and Muhammad Ali Gulzar},
journal= {arXiv preprint arXiv:2307.08672},
year = {2024}
}
Comments
Published in SE4SafeML 2023 (co-located with FSE 2023). See https://dl.acm.org/doi/abs/10.1145/3617574.3617858