mlsanityData to 14 Oct 2026

Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning

arXiv 2110.08394 ↗First posted 15 Oct 2021cs.LGcited by 53

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Abstract (arXiv)

Conventional federated learning (FL) trains one global model for a federation of clients with decentralized data, reducing the privacy risk of centralized training. However, the distribution shift across non-IID datasets, often poses a challenge to this one-model-fits-all solution. Personalized FL aims to mitigate this issue systematically. In this work, we propose APPLE, a personalized cross-silo FL framework that adaptively learns how much each client can benefit from other clients' models. We also introduce a method to flexibly control the focus of training APPLE between global and local objectives. We empirically evaluate our method's convergence and generalization behaviors, and perform extensive experiments on two benchmark datasets and two medical imaging datasets under two non-IID settings. The results show that the proposed personalized FL framework, APPLE, achieves state-of-the-art performance compared to several other personalized FL approaches in the literature. The code is publicly available at https://github.com/ljaiverson/pFL-APPLE.

Key facts. Cited by 53 papers in the map (data through 14 Oct 2026). Builds on 34 of its 43 references that are in the map. First posted on arXiv 15 Oct 2021.

Builds on

34 of 43 references.

References · 34

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  6. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  7. 2018Federated Learning with Non-IID Datacited by 1,108
  8. 2018On First-Order Meta-Learning Algorithmscited by 1,018
  9. 2017A Survey on Multi-Task Learningcited by 840
  10. 2017Federated Multi-Task Learningcited by 672
  11. 2020The Future of Digital Health with Federated Learningcited by 633
  12. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
22 more
  1. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  2. 2019The Liver Tumor Segmentation Benchmark (LiTS)cited by 509
  3. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  4. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 459
  5. 2020Adaptive Personalized Federated Learningcited by 358
  6. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  7. 2021A Field Guide to Federated Optimizationcited by 315
  8. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 312
  9. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 305
  10. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  11. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 269
  12. 2020Federated Learning of a Mixture of Global and Local Modelscited by 210
  13. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  14. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  15. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  16. 2019Adaptive Gradient-Based Meta-Learning Methodscited by 183
  17. 2018Federated Meta-Learning with Fast Convergence and Efficient Communicationcited by 155
  18. 2019Federated Evaluation of On-device Personalizationcited by 147
  19. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  20. 2019Dynamic Curriculum Learning for Imbalanced Data Classificationcited by 93
  21. 2018Deep Neural Network Compression with Single and Multiple Level Quantizationcited by 27
  22. 2019Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learningcited by 18

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 7; 2024: 17; 2025: 18; 2026: 9.

53 papers.

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  1. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  2. 2023Eliminating Domain Bias for Federated Learning in Representation Spacecited by 38
  3. 2023Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaborationcited by 23
  4. 2023PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmarkcited by 17
  5. 2024Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Modelscited by 17
  6. 2024Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Modelscited by 15
  7. 2022PGFed: Personalize Each Client's Global Objective for Federated Learningcited by 13
  8. 2024Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architecturescited by 12
  9. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 9
  10. 2025PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learningcited by 9
  11. 2023HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learningcited by 8
  12. 2025Federated Learning with Domain Shift Erasercited by 7
38 more
  1. 2023Federated Learning of Shareable Bases for Personalization-Friendly Image Classificationcited by 6
  2. 2024Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learningcited by 6
  3. 2024Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuningcited by 6
  4. 2025Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learningcited by 5
  5. 2023Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Datacited by 3
  6. 2024The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learningcited by 3
  7. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  8. 2026Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentationcited by 3
  9. 2024Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformationcited by 2
  10. 2024FedAH: Aggregated Head for Personalized Federated Learningcited by 2
  11. 2024Optimizing Personalized Federated Learning through Adaptive Layer-Wise Learningcited by 2
  12. 2026Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Packcited by 2
  13. 2026FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Spacecited by 2
  14. 2024DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanismcited by 1
  15. 2024Personalized Federated Learning Techniques: Empirical Analysiscited by 1
  16. 2024TPFL: A Trustworthy Personalized Federated Learning Framework via Subjective Logiccited by 1
  17. 2024FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learningcited by 1
  18. 2024Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecturecited by 1
  19. 2025FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detectioncited by 1
  20. 2025Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Controlcited by 1
  21. 2025Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Schedulingcited by 1
  22. 2025FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Datacited by 1
  23. 2026Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Datacited by 1
  24. 2023FedCliP: Federated Learning with Client Pruningcited by 0
  25. 2024Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneitycited by 0
  26. 2024Class-Wise Federated Averaging for Efficient Personalizationcited by 0
  27. 2025FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Datacited by 0
  28. 2025FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservationcited by 0
  29. 2025Query-based Knowledge Transfer for Heterogeneous Learning Environmentscited by 0
  30. 2025FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcarecited by 0
  31. 2025Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Samplingcited by 0
  32. 2025Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learningcited by 0
  33. 2025Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuningcited by 0
  34. 2025FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identificationcited by 0
  35. 2025Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approachcited by 0
  36. 2025Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networkscited by 0
  37. 2026Patient-Conditioned Adaptive Offsets for Reliable Diagnosis across Subgroupscited by 0
  38. 2026Separate Aggregation of Split Network for Personalized Federated Learningcited by 0

50 most cited of 53 All 53 (JSON)