FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, 2023-June, pp. 8042-8052
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation.pdf | Published version | 1.51 MB |
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Federated Learning FL is a distributed learning paradigm that collaboratively learns a global model by multiple clients with data privacy preserving Although many FL algorithms have been proposed for classification tasks few works focus on more challenging semantic segmentation tasks especially in the class heterogeneous FL situation Compared with classification the issues from heterogeneous FL for semantic segmentation are more severe 1 Due to the non IID distribution different clients may contain inconsistent foreground background classes resulting in divergent local updates 2 Class heterogeneity for complex dense prediction tasks makes the local optimum of clients farther from the global optimum In this work we propose FedSeg a basic federated learning approach for class heterogeneous semantic segmentation We first propose a simple but strong modified cross entropy loss to correct the local optimization and address the foreground background inconsistency problem Based on it we introduce pixel level contrastive learning to enforce local pixel embeddings belonging to the global semantic space Extensive experiments on four semantic segmentation benchmarks Cityscapes CamVID PascalVOC and ADE20k demonstrate the effectiveness of our FedSeg We hope this work will attract more attention from the FL community to the challenging semantic segmentation federated learning
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