mlsanityData to 14 Oct 2026

Personalized Federated Learning with Feature Alignment and Classifier Collaboration

arXiv 2306.11867 ↗First posted 20 Jun 2023cs.LG cs.DCcited by 97

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Abstract (arXiv)

Data heterogeneity is one of the most challenging issues in federated learning, which motivates a variety of approaches to learn personalized models for participating clients. One such approach in deep neural networks based tasks is employing a shared feature representation and learning a customized classifier head for each client. However, previous works do not utilize the global knowledge during local representation learning and also neglect the fine-grained collaboration between local classifier heads, which limit the model generalization ability. In this work, we conduct explicit local-global feature alignment by leveraging global semantic knowledge for learning a better representation. Moreover, we quantify the benefit of classifier combination for each client as a function of the combining weights and derive an optimization problem for estimating optimal weights. Finally, extensive evaluation results on benchmark datasets with various heterogeneous data scenarios demonstrate the effectiveness of our proposed method. Code is available at https://github.com/JianXu95/FedPAC

Key facts. Cited by 97 papers in the map (data through 14 Oct 2026). Builds on 54 of its 73 references that are in the map. First posted on arXiv 20 Jun 2023.

Builds on

54 of 73 references.

References · 54

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  7. 2018Federated Learning with Non-IID Datacited by 1,108
  8. 2021Model-Contrastive Federated Learningcited by 766
  9. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  10. 2017Federated Multi-Task Learningcited by 672
  11. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  12. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 640
38 more
  1. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  2. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  3. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  4. 2019Federated Learning with Personalization Layerscited by 541
  5. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
  6. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 519
  7. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  9. 2021Towards Personalized Federated Learningcited by 464
  10. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  11. 2020Adaptive Personalized Federated Learningcited by 358
  12. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  13. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  14. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  15. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  16. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  17. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
  18. 2021Personalized Federated Learning using Hypernetworkscited by 212
  19. 2020Federated Learning of a Mixture of Global and Local Modelscited by 210
  20. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  21. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 192
  22. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 182
  23. 2020Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcited by 154
  24. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  25. 2019Transferrable Prototypical Networks for Unsupervised Domain Adaptationcited by 141
  26. 2020Federated Visual Classification with Real-World Data Distributioncited by 128
  27. 2021Parameterized Knowledge Transfer for Personalized Federated Learningcited by 109
  28. 2021FedMix: Approximation of Mixup under Mean Augmented Federated Learningcited by 108
  29. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  30. 2021FedProc: Prototypical Contrastive Federated Learning on Non-IID datacited by 81
  31. 2021Personalized Federated Learning with Gaussian Processescited by 76
  32. 2021Personalized Federated Learning through Local Memorizationcited by 57
  33. 2022FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Datacited by 50
  34. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  35. 2021Node Selection Toward Faster Convergence for Federated Learning on Non-IID Datacited by 39
  36. 2021Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learningcited by 31
  37. 2021Implicit Gradient Alignment in Distributed and Federated Learningcited by 29
  38. 2022FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learningcited by 27

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Citing papers per year: 2023: 15; 2024: 34; 2025: 32; 2026: 16.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
  2. 2024Selective Aggregation for Low-Rank Adaptation in Federated Learningcited by 80
  3. 2023Advances and Challenges in Meta-Learning: A Technical Reviewcited by 65
  4. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 46
  5. 2024FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuningcited by 27
  6. 2023Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrastcited by 17
  7. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  8. 2024Personalized Federated Learning via Feature Distribution Adaptationcited by 13
  9. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  10. 2024Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRAcited by 11
  11. 2024FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregationcited by 11
  12. 2023ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learningcited by 9
38 more
  1. 2024Decentralized Directed Collaboration for Personalized Federated Learningcited by 9
  2. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  3. 2024Tackling Data Heterogeneity in Federated Learning via Loss Decompositioncited by 9
  4. 2023PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learningcited by 8
  5. 2023FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learningcited by 8
  6. 2024Spectral Co-Distillation for Personalized Federated Learningcited by 8
  7. 2024Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Driftcited by 8
  8. 2025FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learningcited by 8
  9. 2023FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networkscited by 7
  10. 2025Federated Domain Generalization with Data-free On-server Matching Gradientcited by 7
  11. 2023Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selectioncited by 6
  12. 2025Capture Global Feature Statistics for One-Shot Federated Learningcited by 6
  13. 2025Federated Learning for Medical Image Classification: A Comprehensive Benchmarkcited by 6
  14. 2023FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learningcited by 5
  15. 2024FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Qualitycited by 5
  16. 2025Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learningcited by 5
  17. 2023UNIDEAL: Curriculum Knowledge Distillation Federated Learningcited by 4
  18. 2023Navigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detectioncited by 4
  19. 2024FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantificationcited by 4
  20. 2024Personalized Quantum Federated Learning for Privacy Image Classificationcited by 4
  21. 2024F3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristicscited by 4
  22. 2023FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated Learningcited by 3
  23. 2023Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchorcited by 3
  24. 2024Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Modelscited by 3
  25. 2024FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Datacited by 3
  26. 2024Embarrassingly Parallel GFlowNetscited by 3
  27. 2024Towards Layer-Wise Personalized Federated Learning: Adaptive Layer Disentanglement via Conflicting Gradientscited by 3
  28. 2024Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clientscited by 3
  29. 2024FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clientscited by 3
  30. 2025Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusioncited by 3
  31. 2024FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learningcited by 2
  32. 2024Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformationcited by 2
  33. 2024FedAli: Personalized Federated Learning Alignment with Prototype Layers for Generalized Mobile Servicescited by 2
  34. 2025Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learningcited by 2
  35. 2025FedAGHN: Personalized Federated Learning with Attentive Graph HyperNetworkscited by 2
  36. 2025FedBM: Stealing Knowledge from Pre-trained Language Models for Heterogeneous Federated Learningcited by 2
  37. 2025Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learningcited by 2
  38. 2024Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tunecited by 1

50 most cited of 97 All 97 (JSON)