mlsanityData to 14 Oct 2026

FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

arXiv 2307.01217 ↗First posted 1 Jul 2023cs.LG cs.AIcited by 36

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Abstract (arXiv)

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. FedCP is more fine-grained to consider personalization in a sample-specific manner than existing pFL methods. Extensive experiments in computer vision and natural language processing domains show that FedCP outperforms eleven state-of-the-art methods by up to 6.69%. Furthermore, FedCP maintains its superiority when some clients accidentally drop out, which frequently happens in mobile settings. Our code is public at https://github.com/TsingZ0/FedCP.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). Builds on 43 of its 60 references that are in the map. First posted on arXiv 1 Jul 2023.

Builds on

43 of 60 references.

References · 43

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
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31 more
  1. 2019Deep Leakage from Gradientscited by 1,021
  2. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  3. 2016Bag of Tricks for Efficient Text Classificationcited by 876
  4. 2021Model-Contrastive Federated Learningcited by 766
  5. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  6. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  8. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  9. 2019Federated Learning with Personalization Layerscited by 541
  10. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  11. 2021Towards Personalized Federated Learningcited by 464
  12. 2019Federated Machine Learning: Concept and Applicationscited by 412
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  14. 2020Balanced Meta-Softmax for Long-Tailed Visual Recognitioncited by 400
  15. 2017A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasetscited by 377
  16. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  17. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  18. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
  19. 2021Personalized Federated Learning using Hypernetworkscited by 212
  20. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  21. 2018SpotTune: Transfer Learning through Adaptive Fine-tuningcited by 168
  22. 2017Convolutional Networks with Adaptive Inference Graphscited by 148
  23. 2017Convergence Analysis of Two-layer Neural Networks with ReLU Activationcited by 137
  24. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  25. 2019PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representationcited by 114
  26. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 67
  27. 2017Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Executioncited by 59
  28. 2021FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learningcited by 39
  29. 2022LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorizationcited by 26
  30. 2021Connecting Low-Loss Subspace for Personalized Federated Learningcited by 12
  31. 2021TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 12; 2025: 14; 2026: 6.

36 papers.

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  1. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  2. 2024pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learningcited by 24
  3. 2024Personalized Federated Continual Learning via Multi-granularity Promptcited by 20
  4. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  5. 2023PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmarkcited by 17
  6. 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learningcited by 17
  7. 2024Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcarecited by 12
  8. 2025PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learningcited by 9
  9. 2024Federated Representation Learning in the Under-Parameterized Regimecited by 7
  10. 2024Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuningcited by 6
  11. 2025AugFL: Augmenting Federated Learning with Pretrained Modelscited by 6
  12. 2023FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learningcited by 5
24 more
  1. 2025Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Modelscited by 5
  2. 2026Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneitycited by 4
  3. 2025Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusioncited by 3
  4. 2024DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representationscited by 2
  5. 2024Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfercited by 2
  6. 2024FedAH: Aggregated Head for Personalized Federated Learningcited by 2
  7. 2024Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learningcited by 2
  8. 2024Learnable Sparse Customization in Heterogeneous Edge Computingcited by 2
  9. 2025FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworkscited by 2
  10. 2025FedRIR: Rethinking Information Representation in Federated Learningcited by 1
  11. 2025FedAPM: Federated Learning via ADMM with Partial Model Personalizationcited by 1
  12. 2025GraphFedMIG: Tackling Class Imbalance in Federated Graph Learning via Mutual Information-Guided Generationcited by 1
  13. 2026OmniFM: Toward Modality-Robust and Task-Agnostic Federated Learning for Heterogeneous Medical Imagingcited by 1
  14. 2026SubFLOT: Submodel Extraction for Efficient and Personalized Federated Learning via Optimal Transportcited by 1
  15. 2024Adaptive Guidance for Local Training in Heterogeneous Federated Learningcited by 0
  16. 2025Controlled privacy leakage propagation throughout overlapping grouped learningcited by 0
  17. 2025Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learningcited by 0
  18. 2025Whom to Trust? Adaptive Collaboration in Personalized Federated Learningcited by 0
  19. 2025Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspectivecited by 0
  20. 2025Generalizable Federated Learning using Client Adaptive Focal Modulationcited by 0
  21. 2025FedeCouple: Fine-Grained Balancing of Global-Generalization and Local-Adaptability in Federated Learningcited by 0
  22. 2026pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AIcited by 0
  23. 2026HEART-PFL: Stable Personalized Federated Learning under Heterogeneity with Hierarchical Directional Alignment and Adversarial Knowledge Transfercited by 0
  24. 2026Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learningcited by 0