mlsanityData to 7 Oct 2026

MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence

arXiv 2304.01950 ↗First posted 1 Apr 2023cs.LG cs.AI cs.CVcited by 20

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Abstract (arXiv)

Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution among edge clients can impair the local model performance. The existing single prototype-based strategy represents a class by using the mean of the feature space. However, feature spaces are usually not clustered, and a single prototype may not represent a class well. Motivated by this, this paper proposes a multi-prototype federated contrastive learning approach (MP-FedCL) which demonstrates the effectiveness of using a multi-prototype strategy over a single-prototype under non-IID settings, including both label and feature skewness. Specifically, a multi-prototype computation strategy based on textitk-means is first proposed to capture different embedding representations for each class space, using multiple prototypes (k centroids) to represent a class in the embedding space. In each global round, the computed multiple prototypes and their respective model parameters are sent to the edge server for aggregation into a global prototype pool, which is then sent back to all clients to guide their local training. Finally, local training for each client minimizes their own supervised learning tasks and learns from shared prototypes in the global prototype pool through supervised contrastive learning, which encourages them to learn knowledge related to their own class from others and reduces the absorption of unrelated knowledge in each global iteration. Experimental results on MNIST, Digit-5, Office-10, and DomainNet show that our method outperforms multiple baselines, with an average test accuracy improvement of about 4.6% and 10.4% under feature and label non-IID distributions, respectively.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 45 of its 98 references that are in the map. First posted on arXiv 1 Apr 2023.

Builds on

45 of 98 references.

References · 45

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  5. 2019Advances and Open Problems in Federated Learningcited by 3,021
  6. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  7. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  8. 2020Supervised Contrastive Learningcited by 2,803
  9. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  10. 2021Model-Contrastive Federated Learningcited by 762
  11. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  12. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
33 more
  1. 2019Federated Learning with Personalization Layerscited by 533
  2. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  3. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  4. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  5. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  6. 2020Learning to Generate Novel Domains for Domain Generalizationcited by 296
  7. 2019Asynchronous Federated Optimizationcited by 264
  8. 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 264
  9. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  10. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  11. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
  12. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 141
  13. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  14. 2022Federated Learning with Label Distribution Skew via Logits Calibrationcited by 108
  15. 2021Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representationscited by 96
  16. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  17. 2018Wireless Network Intelligence at the Edgecited by 90
  18. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  19. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 80
  20. 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 75
  21. 2023A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AIcited by 68
  22. 2022How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learningcited by 65
  23. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  24. 2015Towards Universal Paraphrastic Sentence Embeddingscited by 59
  25. 2022Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCocited by 52
  26. 2021Dynamic Network Embedding Surveycited by 44
  27. 2015Metric Learning with Adaptive Density Discriminationcited by 40
  28. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  29. 2020Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learningcited by 37
  30. 2021Prototype Guided Federated Learning of Visual Feature Representationscited by 22
  31. 2022Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustnesscited by 20
  32. 2019Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costscited by 12
  33. 2023Prototype Helps Federated Learning: Towards Faster Convergencecited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 8; 2025: 5; 2026: 3.

20 papers.

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  1. 2023Faster Segment Anything: Towards Lightweight SAM for Mobile Applicationscited by 345
  2. 2023A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineeringcited by 45
  3. 2023Robustness of SAM: Segment Anything Under Corruptions and Beyondcited by 30
  4. 2024Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modalitycited by 21
  5. 2025DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Surveycited by 6
  6. 2024Towards Robust Federated Learning via Logits Calibration on Non-IID Datacited by 5
  7. 2024FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneitycited by 5
  8. 2024Lite-SAM Is Actually What You Need for Segment Everythingcited by 5
  9. 2023Boosting Federated Learning Convergence with Prototype Regularizationcited by 4
  10. 2024FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Qualitycited by 4
  11. 2024Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Datacited by 4
  12. 2024Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architecturescited by 3
8 more
  1. 2024Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networkscited by 2
  2. 2025FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Driftcited by 2
  3. 2025Mitigating Domain Shift in Federated Learning via Intra- and Inter-Domain Prototypescited by 1
  4. 2025Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligencecited by 1
  5. 2025FedFeat+: A Robust Federated Learning Framework Through Federated Aggregation and Differentially Private Feature-Based Classifier Retrainingcited by 1
  6. 2026Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clientscited by 0
  7. 2026FedDAP: Domain-Aware Prototype Learning for Federated Learning under Domain Shiftcited by 0
  8. 2026FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Driftcited by 0