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How To Backdoor Federated Learning
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Federated learning enables thousands of participants to construct a deep learning model without sharing their private training data with each other. For example, multiple smartphones can jointly train a next-word predictor for keyboards without revealing what individual users type. We demonstrate that any participant in federated learning can introduce hidden backdoor functionality into the joint global model, e.g., to ensure that an image classifier assigns an attacker-chosen label to images with certain features, or that a word predictor completes certain sentences with an attacker-chosen word. We design and evaluate a new model-poisoning methodology based on model replacement. An attacker selected in a single round of federated learning can cause the global model to immediately reach 100% accuracy on the backdoor task. We evaluate the attack under different assumptions for the standard federated-learning tasks and show that it greatly outperforms data poisoning. Our generic constrain-and-scale technique also evades anomaly detection-based defenses by incorporating the evasion into the attacker's loss function during training.
Forward citations
Cited by 4 Pith papers
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TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement
TriShield combines artifact detection, Adam momentum pre-entanglement, and SVD task-subspace projection to drive NeuroImprint reconstruction to 0% with claimed near-zero utility loss.
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HADES: Privacy-Preserving Federated Learning via Selective Feature Encryption and Hybrid Model Fusion
HADES selectively encrypts privacy-sensitive features identified by PCA in federated learning, trains hybrid encrypted and plaintext networks, and fuses them to match vanilla FL accuracy with reduced overhead and bett...
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TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement
TriShield claims to eliminate NeuroImprint-style privacy backdoors in federated fine-tuning, but its full-scale results are projections and its key theorems assume the very conditions they need to establish.
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PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction
A federated, privacy-preserving RDSN framework for encrypted image reconstruction whose local differential privacy mechanism is not actually differentially private because it releases low-frequency DCT coefficients wi...
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