mlsanityData to 7 Oct 2026

FedALA: Adaptive Local Aggregation for Personalized Federated Learning

arXiv 2212.01197 ↗First posted 2 Dec 2022cs.LG cs.AIcited by 121

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Abstract (arXiv)

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). Builds on 27 of its 36 references that are in the map. First posted on arXiv 2 Dec 2022.

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27 of 36 references.

References · 27

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  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2019Advances and Open Problems in Federated Learningcited by 3,021
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  6. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
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  11. 2019Deep Leakage from Gradientscited by 1,004
  12. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
15 more
  1. 2016Bag of Tricks for Efficient Text Classificationcited by 869
  2. 2021Model-Contrastive Federated Learningcited by 762
  3. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  4. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
  5. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  6. 2020Federated Learning with Matched Averagingcited by 564
  7. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  8. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  9. 2017A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasetscited by 374
  10. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  11. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  12. 2021Personalized Federated Learning using Hypernetworkscited by 210
  13. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  14. 2019Adaptive Gradient Methods with Dynamic Bound of Learning Ratecited by 181
  15. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53

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Citing papers per year: 2023: 15; 2024: 45; 2025: 40; 2026: 21.

121 papers.

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  1. 2023No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifiercited by 55
  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. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  5. 2023FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policycited by 36
  6. 2023GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learningcited by 18
  7. 2024An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learningcited by 17
  8. 2023PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmarkcited by 16
  9. 2024Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Modelscited by 15
  10. 2024Personalized Federated Learning via Feature Distribution Adaptationcited by 13
  11. 2024Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learningcited by 9
  12. 2024FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharingcited by 8
38 more
  1. 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuningcited by 8
  2. 2025PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learningcited by 8
  3. 2024Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradientscited by 7
  4. 2024Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Frameworkcited by 6
  5. 2024FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentationcited by 6
  6. 2024Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learningcited by 6
  7. 2024Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuningcited by 6
  8. 2024Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRIcited by 5
  9. 2025Differentially Private Federated Learning With Time-Adaptive Privacy Spendingcited by 5
  10. 2025Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learningcited by 5
  11. 2023Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregationcited by 4
  12. 2023UNIDEAL: Curriculum Knowledge Distillation Federated Learningcited by 4
  13. 2024Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosiscited by 4
  14. 2024Towards Satellite Non-IID Imagery: A Spectral Clustering-Assisted Federated Learning Approachcited by 4
  15. 2024F3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristicscited by 4
  16. 2025Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum Sharing and Complementing Perspectivecited by 4
  17. 2023PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learningcited by 3
  18. 2023Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchorcited by 3
  19. 2024Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Modelscited by 3
  20. 2024FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Updatecited by 3
  21. 2024FedCoSR: Personalized Federated Learning with Contrastive Shareable Representations for Label Heterogeneity in Non-IID Datacited by 3
  22. 2024Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graphcited by 3
  23. 2025FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learningcited by 3
  24. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  25. 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
  26. 2024Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformationcited by 2
  27. 2024Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfercited by 2
  28. 2024FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Datacited by 2
  29. 2024FedAH: Aggregated Head for Personalized Federated Learningcited by 2
  30. 2024Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learningcited by 2
  31. 2025Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learningcited by 2
  32. 2025Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environmentscited by 2
  33. 2023The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformerscited by 1
  34. 2023PAGE: Equilibrate Personalization and Generalization in Federated Learningcited by 1
  35. 2024Federated Knowledge Graph Unlearning via Diffusion Modelcited by 1
  36. 2024Decentralized Personalized Federated Learningcited by 1
  37. 2024Jigsaw Game: Federated Clusteringcited by 1
  38. 2024DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanismcited by 1

50 most cited of 121 All 121 (JSON)