Robust hierarchical federated learning with dual-layer filtering mechanism
Hierarchical federated learning (HFL) is an effective “cloud-edge-device” distributed model training framework that protects data privacy. During HFL training, poisoning attacks on local data and transmitted models can affect the accuracy of the global model. Existing methods for defending against u...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-03-01
|
| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/17298806251322260 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714784370425856 |
|---|---|
| author | Chaoyi Yang Wei Liang Yuxiang Chen Jiahong Xiao Dacheng He Tianxiong Liu Jin Wang Amr Tolba |
| author_facet | Chaoyi Yang Wei Liang Yuxiang Chen Jiahong Xiao Dacheng He Tianxiong Liu Jin Wang Amr Tolba |
| author_sort | Chaoyi Yang |
| collection | DOAJ |
| description | Hierarchical federated learning (HFL) is an effective “cloud-edge-device” distributed model training framework that protects data privacy. During HFL training, poisoning attacks on local data and transmitted models can affect the accuracy of the global model. Existing methods for defending against unauthorized attacks primarily rely on single-feature anomaly detection approaches, such as calculating distances or densities between data or model parameters. These methods fail to integrate multiple characteristics and metrics to capture anomalous model updates,thus exhibiting significant limitations in model robustness and accuracy. Therefore, we propose a robust hierarchical federated learning method with a dual-layer filtering mechanism (DF-HFL). This method first uses Kernel density estimation to infer the approximate data distribution of devices, ensuring minimal differences among devices within each cluster. It then calculates the density weight of each cluster and the local weight of each device, comparing the difference between local and global weights. Anomalous weights are filtered through a threshold during aggregation. DF-HFL amplifies the distance between malicious updates and normal updates using the dual-layer filtering mechanism, effectively identifying anomalous weights that do not significantly deviate from the normal weight distribution. This helps in accurately detecting anomalies. Additionally, mean filtering is employed to reduce the impact of anomalous data on the original normal gradients, enhancing system robustness. To demonstrate the effectiveness of the proposed method, experiments were conducted on MNIST, FMNIST, Heart Disease and Bank Market datasets. Compared to existing methods like FedAvg, Krum, Random, and Multi-Krum, the global model accuracy improved by 5.18%, 38.98%, 29.96%, and 6.44%. |
| format | Article |
| id | doaj-art-ffada7bda1b949d0a701a26971b768ec |
| institution | DOAJ |
| issn | 1729-8814 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj-art-ffada7bda1b949d0a701a26971b768ec2025-08-20T03:13:36ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142025-03-012210.1177/17298806251322260Robust hierarchical federated learning with dual-layer filtering mechanismChaoyi Yang0Wei Liang1Yuxiang Chen2Jiahong Xiao3Dacheng He4Tianxiong Liu5Jin Wang6Amr Tolba7 Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, China Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, China Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, China Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, China Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, China , Changsha, China Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan, China Computer Science Department, Community College, , Riyadh Saudi ArabiaHierarchical federated learning (HFL) is an effective “cloud-edge-device” distributed model training framework that protects data privacy. During HFL training, poisoning attacks on local data and transmitted models can affect the accuracy of the global model. Existing methods for defending against unauthorized attacks primarily rely on single-feature anomaly detection approaches, such as calculating distances or densities between data or model parameters. These methods fail to integrate multiple characteristics and metrics to capture anomalous model updates,thus exhibiting significant limitations in model robustness and accuracy. Therefore, we propose a robust hierarchical federated learning method with a dual-layer filtering mechanism (DF-HFL). This method first uses Kernel density estimation to infer the approximate data distribution of devices, ensuring minimal differences among devices within each cluster. It then calculates the density weight of each cluster and the local weight of each device, comparing the difference between local and global weights. Anomalous weights are filtered through a threshold during aggregation. DF-HFL amplifies the distance between malicious updates and normal updates using the dual-layer filtering mechanism, effectively identifying anomalous weights that do not significantly deviate from the normal weight distribution. This helps in accurately detecting anomalies. Additionally, mean filtering is employed to reduce the impact of anomalous data on the original normal gradients, enhancing system robustness. To demonstrate the effectiveness of the proposed method, experiments were conducted on MNIST, FMNIST, Heart Disease and Bank Market datasets. Compared to existing methods like FedAvg, Krum, Random, and Multi-Krum, the global model accuracy improved by 5.18%, 38.98%, 29.96%, and 6.44%.https://doi.org/10.1177/17298806251322260 |
| spellingShingle | Chaoyi Yang Wei Liang Yuxiang Chen Jiahong Xiao Dacheng He Tianxiong Liu Jin Wang Amr Tolba Robust hierarchical federated learning with dual-layer filtering mechanism International Journal of Advanced Robotic Systems |
| title | Robust hierarchical federated learning with dual-layer filtering mechanism |
| title_full | Robust hierarchical federated learning with dual-layer filtering mechanism |
| title_fullStr | Robust hierarchical federated learning with dual-layer filtering mechanism |
| title_full_unstemmed | Robust hierarchical federated learning with dual-layer filtering mechanism |
| title_short | Robust hierarchical federated learning with dual-layer filtering mechanism |
| title_sort | robust hierarchical federated learning with dual layer filtering mechanism |
| url | https://doi.org/10.1177/17298806251322260 |
| work_keys_str_mv | AT chaoyiyang robusthierarchicalfederatedlearningwithduallayerfilteringmechanism AT weiliang robusthierarchicalfederatedlearningwithduallayerfilteringmechanism AT yuxiangchen robusthierarchicalfederatedlearningwithduallayerfilteringmechanism AT jiahongxiao robusthierarchicalfederatedlearningwithduallayerfilteringmechanism AT dachenghe robusthierarchicalfederatedlearningwithduallayerfilteringmechanism AT tianxiongliu robusthierarchicalfederatedlearningwithduallayerfilteringmechanism AT jinwang robusthierarchicalfederatedlearningwithduallayerfilteringmechanism AT amrtolba robusthierarchicalfederatedlearningwithduallayerfilteringmechanism |