mlsanityData to 7 Oct 2026

Layer-wised Model Aggregation for Personalized Federated Learning

arXiv 2205.03993 ↗First posted 9 May 2022cs.DCcited by 95

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Abstract (arXiv)

Personalized Federated Learning (pFL) not only can capture the common priors from broad range of distributed data, but also support customized models for heterogeneous clients. Researches over the past few years have applied the weighted aggregation manner to produce personalized models, where the weights are determined by calibrating the distance of the entire model parameters or loss values, and have yet to consider the layer-level impacts to the aggregation process, leading to lagged model convergence and inadequate personalization over non-IID datasets. In this paper, we propose a novel pFL training framework dubbed Layer-wised Personalized Federated learning (pFedLA) that can discern the importance of each layer from different clients, and thus is able to optimize the personalized model aggregation for clients with heterogeneous data. Specifically, we employ a dedicated hypernetwork per client on the server side, which is trained to identify the mutual contribution factors at layer granularity. Meanwhile, a parameterized mechanism is introduced to update the layer-wised aggregation weights to progressively exploit the inter-user similarity and realize accurate model personalization. Extensive experiments are conducted over different models and learning tasks, and we show that the proposed methods achieve significantly higher performance than state-of-the-art pFL methods.

Key facts. Cited by 95 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 May 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 9; 2023: 30; 2024: 26; 2025: 25; 2026: 5.

95 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2023Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generationcited by 54
  3. 2024FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learningcited by 47
  4. 2024LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinementcited by 37
  5. 2022FedTP: Federated Learning by Transformer Personalizationcited by 36
  6. 2022FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformerscited by 33
  7. 2023L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learningcited by 29
  8. 2024Harmonizing Generalization and Personalization in Federated Prompt Learningcited by 26
  9. 2023Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaborationcited by 23
  10. 2023Layer-wise Linear Mode Connectivitycited by 22
  11. 2023PeFLL: Personalized Federated Learning by Learning to Learncited by 21
  12. 2025FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectorscited by 19
38 more
  1. 2023FedHCA2: Towards Hetero-Client Federated Multi-Task Learningcited by 17
  2. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  3. 2023FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noisecited by 14
  4. 2024Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregationcited by 13
  5. 2024Federated Online Adaptation for Deep Stereocited by 12
  6. 2023Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Applicationcited by 11
  7. 2023Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classificationcited by 10
  8. 2023pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learningcited by 9
  9. 2023G2uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clusteringcited by 9
  10. 2024Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detectioncited by 9
  11. 2024Tackling Data Heterogeneity in Federated Learning via Loss Decompositioncited by 9
  12. 2023Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learningcited by 8
  13. 2024FedUV: Uniformity and Variance for Heterogeneous Federated Learningcited by 8
  14. 2023A Survey of Federated Evaluation in Federated Learningcited by 7
  15. 2023NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clientscited by 7
  16. 2023Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learningcited by 7
  17. 2025Federated Learning with Domain Shift Erasercited by 7
  18. 2022Personalized Federated Learning with Communication Compressioncited by 6
  19. 2023Federated Learning of Shareable Bases for Personalization-Friendly Image Classificationcited by 6
  20. 2023Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verificationcited by 6
  21. 2024Federated Class-Incremental Learning with Hierarchical Generative Prototypescited by 6
  22. 2025FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinkingcited by 6
  23. 2022Federated Fuzzy Neural Network with Evolutionary Rule Learningcited by 5
  24. 2023Federated Class-Incremental Learning with Promptingcited by 5
  25. 2024Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devicescited by 5
  26. 2023Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonizationcited by 4
  27. 2024Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classificationcited by 4
  28. 2024Personalized Quantum Federated Learning for Privacy Image Classificationcited by 4
  29. 2025Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Datacited by 4
  30. 2022Federated Learning with Intermediate Representation Regularizationcited by 3
  31. 2023Digital Twin-Assisted Knowledge Distillation Framework for Heterogeneous Federated Learningcited by 3
  32. 2023Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clientscited by 3
  33. 2024Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Trainingcited by 3
  34. 2024The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learningcited by 3
  35. 2023Federated learning-outcome prediction with multi-layer privacy protectioncited by 2
  36. 2024FedSheafHN: Personalized Federated Learning on Graph-structured Datacited by 2
  37. 2024Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformationcited by 2
  38. 2024Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgerycited by 2

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