Mitigating Backdoor Attacks in Federated Learning

Abstract

Malicious clients can attack federated learning systems using malicious data, including backdoor samples, during the training phase. The compromised global model will perform well on the validation dataset designed for the task, but a small subset of data with backdoor patterns may trigger the model to make a wrong prediction. There has been an arms race between attackers who tried to conceal attacks and defenders who tried to detect attacks during the aggregation stage of training on the server-side. In this work, we propose a new and effective method to mitigate backdoor attacks after the training phase. Specifically, we design a federated pruning method to remove redundant neurons in the network and then adjust the model's extreme weight values. Our experiments conducted on distributed Fashion-MNIST show that our method can reduce the average attack success rate from 99.7% to 1.9% with a 5.5% loss of test accuracy on the validation dataset. To minimize the pruning influence on test accuracy, we can fine-tune after pruning, and the attack success rate drops to 6.4%, with only a 1.7% loss of test accuracy. Further experiments under Distributed Backdoor Attacks on CIFAR-10 also show promising results that the average attack success rate drops more than 70% with less than 2% loss of test accuracy on the validation dataset.

References in corpus (8)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Communication-Efficient Learning of Deep Networks from Decentralized Data
• BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
• How To Backdoor Federated Learning
• Can You Really Backdoor Federated Learning?
• Mitigating Sybils in Federated Learning Poisoning
• Learning to Detect Malicious Clients for Robust Federated Learning
• Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation

Cited by in corpus (6)

• Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
• A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
• Self-supervised Cross-silo Federated Neural Architecture Search
• Hijack Vertical Federated Learning Models As One Party
• ABC-FL: Anomalous and Benign client Classification in Federated Learning
• Security and Privacy Issues and Solutions in Federated Learning for Digital Healthcare