mlsanityData to 7 Oct 2026

Decentralized Federated Learning through Proxy Model Sharing

arXiv 2111.11343 ↗First posted 22 Nov 2021cs.LGcited by 56

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Abstract (arXiv)

Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator's data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant's privacy. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). Builds on 43 of its 79 references that are in the map. First posted on arXiv 22 Nov 2021.

Builds on

43 of 79 references.

References · 43

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  3. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
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31 more
  1. 2018The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networkscited by 847
  2. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  3. 2017Renyi Differential Privacycited by 643
  4. 2017Deep Mutual Learningcited by 621
  5. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 509
  6. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  7. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 347
  8. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  9. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
  10. 2018Distributed learning of deep neural network over multiple agentscited by 277
  11. 2020Auditing Differentially Private Machine Learning: How Private is Private SGD?cited by 213
  12. 2019R'enyi Differential Privacy of the Sampled Gaussian Mechanismcited by 199
  13. 2021Adversary Instantiation: Lower Bounds for Differentially Private Machine Learningcited by 193
  14. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  15. 2018Stochastic Gradient Push for Distributed Deep Learningcited by 152
  16. 2018Protection Against Reconstruction and Its Applications in Private Federated Learningcited by 127
  17. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  18. 2017Network Topology and Communication-Computation Tradeoffs in Decentralized Optimizationcited by 119
  19. 2019Hypothesis Testing Interpretations and Renyi Differential Privacycited by 84
  20. 2020Federated Mutual Learningcited by 70
  21. 2019Differentially Private Model Publishing for Deep Learningcited by 69
  22. 2020Siloed Federated Learning for Multi-Centric Histopathology Datasetscited by 61
  23. 2020Federated Learning for Computational Pathology on Gigapixel Whole Slide Imagescited by 54
  24. 2020Decentralized Federated Learning via Mutual Knowledge Transfercited by 42
  25. 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
  26. 2019Yottixel -- An Image Search Engine for Large Archives of Histopathology Whole Slide Imagescited by 41
  27. 2020Federated Model Distillation with Noise-Free Differential Privacycited by 40
  28. 2014Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphscited by 31
  29. 2019Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligencecited by 26
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  31. 2020Adaptive Distillation for Decentralized Learning from Heterogeneous Clientscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 13; 2024: 24; 2025: 8; 2026: 7.

56 papers.

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  1. 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  3. 2023Deep Model Fusion: A Surveycited by 89
  4. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  5. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  6. 2024Byzantine-Robust Decentralized Federated Learningcited by 47
  7. 2022Disparate Impact in Differential Privacy from Gradient Misalignmentcited by 31
  8. 2023A Survey on Decentralized Federated Learningcited by 27
  9. 2024Federated Model Heterogeneous Matryoshka Representation Learningcited by 21
  10. 2024FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathologycited by 17
  11. 2023Self-supervised Representation Learning From Random Data Projectorscited by 14
  12. 2024Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challengescited by 14
38 more
  1. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  2. 2022Find Your Friends: Personalized Federated Learning with the Right Collaboratorscited by 10
  3. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  4. 2024Tackling Data Heterogeneity in Federated Learning via Loss Decompositioncited by 9
  5. 2023Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMIcited by 6
  6. 2023Stochastic Unrolled Federated Learningcited by 6
  7. 2024A survey on secure decentralized optimization and learningcited by 4
  8. 2025Toward Malicious Clients Detection in Federated Learningcited by 4
  9. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  10. 2023FedSODA: Federated Cross-assessment and Dynamic Aggregation for Histopathology Segmentationcited by 3
  11. 2024Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Siloscited by 3
  12. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  13. 2023Sparse Decentralized Federated Learningcited by 2
  14. 2023FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregationcited by 2
  15. 2024Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Studycited by 2
  16. 2024Practical Insights into Knowledge Distillation for Pre-Trained Modelscited by 2
  17. 2024Cyclical Weight Consolidation: Towards Solving Catastrophic Forgetting in Serial Federated Learningcited by 2
  18. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  19. 2024Blockchain Meets LLMs: A Living Survey on Bidirectional Integrationcited by 2
  20. 2024Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosiscited by 2
  21. 2023Collaborative Distributed Machine Learningcited by 1
  22. 2024In-depth Analysis of Privacy Threats in Federated Learning for Medical Datacited by 1
  23. 2024FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learningcited by 1
  24. 2024Exposing the Vulnerability of Decentralized Learning to Membership Inference Attacks Through the Lens of Graph Mixingcited by 1
  25. 2025Decentralized Federated Dataset Dictionary Learning for Multi-Source Domain Adaptationcited by 1
  26. 2026A Secure and Private Distributed Bayesian Federated Learning Designcited by 1
  27. 2023Federated Learning: Organizational Opportunities, Challenges, and Adoption Strategiescited by 0
  28. 2024MAPL: Model Agnostic Peer-to-peer Learningcited by 0
  29. 2024P4: Towards private, personalized, and Peer-to-Peer learningcited by 0
  30. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  31. 2024Trust-free Personalized Decentralized Learningcited by 0
  32. 2024How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?cited by 0
  33. 2025Topology-Aware Knowledge Propagation in Decentralized Learningcited by 0
  34. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  35. 2025Realistic Urban Traffic Generator using Decentralized Federated Learning for the SUMO simulatorcited by 0
  36. 2025Client Clustering Meets Knowledge Sharing: Enhancing Privacy and Robustness in Personalized Peer-to-Peer Learningcited by 0
  37. 2025Decentralized Federated Learning of Probabilistic Generative Classifierscited by 0
  38. 2025Trading Vector Data in Vector Databasescited by 0

50 most cited of 56 All 56 (JSON)