Energy-based Backdoor Defense Against Federated Graph Learning
Authors: Guancheng Wan, Zitong Shi, Wenke Huang, Guibin Zhang, Dacheng Tao, Mang Ye
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results on various settings of federated graph scenarios under backdoor attacks validate the effectiveness of this approach. We conducted experiments on five mainstream datasets under both IID and Non-IID scenarios, as well as with varying proportions of malicious clients. The results demonstrate that our approach outperforms the current state-of-the-art methods in traditional FL. We plotted the curves of V during the training process on the Pubmed dataset with a malicious proportion of Υ = 0.3 and 0.5. We conducted an ablation study on the key components of our method using the Cora and Pub Med datasets. |
| Researcher Affiliation | Academia | 1 National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University 2 Taikang Center for Life and Medical Sciences, Wuhan University 3 Tongji University 4 Generative AI Lab, College of Computing and Data Science, Nanyang Technological University |
| Pseudocode | Yes | Algorithm 1 Fed TGE Input: Communication rounds T, participant scale K, kth client private model wk, and local data Gk Output: The final global model MT for t = 1, 2, , T do Client Side: for k = 1 to K in parallel do fk( ) Local Updating(wt, Gk) // Original training strategy f t k( ) Energy Calibrating(fk( ), Gk) // Injecting distribution knowledge Et k = {E(f t k(vi))}Nk i=1 // Calculating energy elements Server Side: // Cluster and find the cluster with the smaller mean {Et a, Et b} and {Et k}N k =a,b where mean(Et a, Et b) mean({Et k}N k =a,b) St = {St mn}N mn = cos(Et m, Et n)m,n =a,b // Calculating energy elements similarity Ge = (Ve, Ee) (τ, St) // Constructing energy graph Et k (βt k, {Et k}N k =a,b) // Energy graph similarity propagation It k (βt k, {Et k }N k =a,b) // Energy disparity aggregation wt+1 = PN l=1 Ikwt k // Model parameter update |
| Open Source Code | Yes | The code is available at https://github.com/Zitong Shi/fed TGE. |
| Open Datasets | Yes | We evaluate the efficacy and robustness in three scenarios: Citation Network (Yang et al., 2016a), Co-authorship (Shchur et al., 2018), and Amz-purchase (Mc Auley et al., 2015). Detailed information about the datasets is provided in appendix A. Table 4: Statistics of datasets used in experiments. Dataset #Nodes #Edges #Classes #Features Cora 2,708 5,278 7 1,433 Pubmed 19,717 44,324 3 500 Coauthor-CS 18,333 327,576 15 6,805 Amz-Physics 34,493 495,924 5 8415 Amz-Photo 7,650 287,326 8 745 |
| Dataset Splits | Yes | Dataset Split: In this paper, we conduct experiments on the node classification task. Following (Xu et al., 2021; Zheng et al., 2023), we partition the original training dataset (labeled) into training, validation, testing sets, comprising 60%, 20%, and 20% of the total nodes, respectively. |
| Hardware Specification | No | The supercomputing system at the Supercomputing Center of Wuhan University supported the numerical calculations in this paper. No specific hardware models (e.g., GPU/CPU models, memory details) are mentioned. |
| Software Dependencies | No | The Adam optimizer (Kingma, 2014) with a learning rate of 0.01 is used to train the GNN models. No specific version numbers for software libraries (e.g., PyTorch, TensorFlow) or programming languages (e.g., Python) are provided. |
| Experiment Setup | Yes | Training Setting: We repeat each experiment five times for each federated approaches to ensure the robustness and reliability of the results. The Adam optimizer (Kingma, 2014) with a learning rate of 0.01 is used to train the GNN models. Network Structure: Following the common approach in FGL(Dai et al., 2023), we utilize GCN as the 2 layers feature extractor and classifier, with the hidden layer size of 32 for all datasets. Backdoor Attack: We set the malicious client ratio Υ as {0.1, 0.3, 0.5}. Hyper-Parameters: We observed that the choice of hyperparameters does not lead to significant fluctuations in the evaluation metrics. In most of our experiments, we set the default values as 10 for energy-epochs and 0.8 for τ. |