FedProc: Prototypical Contrastive Federated Learning on Non-IID data
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Abstract (arXiv)
Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of all clients are not independent and identically distributed (i.e., non-IID), it is challenging to implement this form of efficient collaborative learning. Although significant efforts have been dedicated to addressing this challenge, the effect on the image classification task is still not satisfactory. In this paper, we propose FedProc: prototypical contrastive federated learning, which is a simple and effective federated learning framework. The key idea is to utilize the prototypes as global knowledge to correct the local training of each client. We design a local network architecture and a global prototypical contrastive loss to regulate the training of local models, which makes local objectives consistent with the global optima. Eventually, the converged global model obtains a good performance on non-IID data. Experimental results show that, compared to state-of-the-art federated learning methods, FedProc improves the accuracy by 1.6%~7.9% with acceptable computation cost.
Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). Builds on 41 of its 55 references that are in the map. First posted on arXiv 25 Sep 2021.
Builds on
41 of 55 references.
References · 41
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- 2020Evaluating the Communication Efficiency in Federated Learning Algorithmscited by 9
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Citing papers per year: 2022: 9; 2023: 17; 2024: 22; 2025: 19; 2026: 13.
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- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
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- 2026FedProIn: Mitigating Client Drift for Learnable Prototypes in Federated Medical Imaging5 Jul 2026
- 2026Enhancing Federated Quadruplet Learning: Stochastic Client Selection and Embedding Stability Analysis8 May 2026
- 2026FedDBP: Enhancing Federated Prototype Learning with Dual-Branch Features and Personalized Global Fusion31 Mar 2026
- 2026FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data14 Mar 2026
- 2026Personalized Federated Learning via Gaussian Generative Modeling12 Mar 2026
- 2026A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System10 Mar 2026
- 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation5 Mar 2026
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