mlsanityData to 14 Oct 2026

Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data

arXiv 2308.03457 ↗First posted 7 Aug 2023cs.LG cs.CVcited by 35

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Abstract (arXiv)

Federated Learning aims to learn a global model on the server side that generalizes to all clients in a privacy-preserving manner, by leveraging the local models from different clients. Existing solutions focus on either regularizing the objective functions among clients or improving the aggregation mechanism for the improved model generalization capability. However, their performance is typically limited by the dataset biases, such as the heterogeneous data distributions and the missing classes. To address this issue, this paper presents a cross-silo prototypical calibration method (FedCSPC), which takes additional prototype information from the clients to learn a unified feature space on the server side. Specifically, FedCSPC first employs the Data Prototypical Modeling (DPM) module to learn data patterns via clustering to aid calibration. Subsequently, the cross-silo prototypical calibration (CSPC) module develops an augmented contrastive learning method to improve the robustness of the calibration, which can effectively project cross-source features into a consistent space while maintaining clear decision boundaries. Moreover, the CSPC module's ease of implementation and plug-and-play characteristics make it even more remarkable. Experiments were conducted on four datasets in terms of performance comparison, ablation study, in-depth analysis and case study, and the results verified that FedCSPC is capable of learning the consistent features across different data sources of the same class under the guidance of calibrated model, which leads to better performance than the state-of-the-art methods. The source codes have been released at https://github.com/qizhuang-qz/FedCSPC.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 30 of its 58 references that are in the map. First posted on arXiv 7 Aug 2023.

Builds on

30 of 58 references.

References · 30

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  5. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2020Federated Learning with Matched Averagingcited by 564
  8. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  9. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  10. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  11. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  12. 2019One-Shot Federated Learningcited by 125
18 more
  1. 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
  2. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  3. 2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
  4. 2021Preservation of the Global Knowledge by Not-True Distillation in Federated Learningcited by 87
  5. 2021Deconfounded Video Moment Retrieval with Causal Interventioncited by 84
  6. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 81
  7. 2021Towards Fair Federated Learning with Zero-Shot Data Augmentationcited by 64
  8. 2022Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Featurescited by 63
  9. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
  10. 2020Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrievalcited by 47
  11. 2022Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learningcited by 46
  12. 2022A Survey on Heterogeneous Federated Learningcited by 42
  13. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  14. 2019Person Re-identification with Metric Learning using Privileged Informationcited by 20
  15. 2023Triple Sequence Learning for Cross-domain Recommendationcited by 19
  16. 2020Multi-source Domain Adaptation for Visual Sentiment Classificationcited by 16
  17. 2022Meta-Causal Feature Learning for Out-of-Distribution Generalizationcited by 14
  18. 2023Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 21; 2026: 7.

35 papers.

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  1. 2025Class-wise Balancing Data Replay for Federated Class-Incremental Learningcited by 9
  2. 2025Fair Deepfake Detectors Can Generalizecited by 7
  3. 2025VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learningcited by 7
  4. 2025Resource-Constrained Federated Continual Learning: What Does Matter?cited by 5
  5. 2026Rethinking LoRA for Privacy-Preserving Federated Learning in Large Modelscited by 5
  6. 2025Federated Out-of-Distribution Generalization: A Causal Augmentation Viewcited by 4
  7. 2025Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalizationcited by 4
  8. 2025Generalized Category Discovery under Domain Shift: A Frequency Domain Perspectivecited by 4
  9. 2025Global Intervention and Distillation for Federated Out-of-Distribution Generalizationcited by 3
  10. 2025ProtoConNet: Prototypical Augmentation and Alignment for Open-Set Few-Shot Image Classificationcited by 3
  11. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  12. 2024Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformationcited by 2
23 more
  1. 2024DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representationscited by 2
  2. 2025FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learningcited by 2
  3. 2025Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learningcited by 2
  4. 2025Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classificationcited by 2
  5. 2025C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learningcited by 2
  6. 2025Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learningcited by 2
  7. 2025Geometric Prior-Guided Federated Prompt Calibrationcited by 2
  8. 2026DVLA-RL: Dual-Level Vision-Language Alignment with Reinforcement Learning Gating for Few-Shot Learningcited by 2
  9. 2024Federated Cross-Training Learners for Robust Generalization under Data Heterogeneitycited by 1
  10. 2024Robust Visual Tracking via Iterative Gradient Descent and Threshold Selectioncited by 1
  11. 2024Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignmentcited by 1
  12. 2025Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidancecited by 1
  13. 2026DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Modelscited by 1
  14. 2026FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learningcited by 1
  15. 2024Comparative Study of Neighbor-based Methods for Local Outlier Detectioncited by 0
  16. 2024Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labelscited by 0
  17. 2025Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learningcited by 0
  18. 2025Towards Initialization-Agnostic Clustering with Iterative Adaptive Resonance Theorycited by 0
  19. 2025A Survey of Personalized Federated Foundation Models for Privacy-Preserving Recommendationcited by 0
  20. 2025Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharingcited by 0
  21. 2026From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneitycited by 0
  22. 2026FedUP: One-Shot Federated Unlearning via Centroid-Guided Plug-in Filterscited by 0
  23. 2026Prototype-guided Bilateral Alignment Multimodal Federated Learningcited by 0