mlsanityData to 7 Oct 2026

Personalized and Private Peer-to-Peer Machine Learning

arXiv 1705.08435 ↗First posted 23 May 2017cs.LG cs.CR cs.DCcited by 54

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Abstract (arXiv)

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully decentralized (peer-to-peer) and asynchronous fashion, with provable convergence rate. We show how to make the algorithm differentially private to protect against the disclosure of information about the personal datasets, and formally analyze the trade-off between utility and privacy. Our experiments show that our approach dramatically outperforms previous work in the non-private case, and that under privacy constraints, we can significantly improve over models learned in isolation.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2017.

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Citing papers per year: 2020: 1; 2021: 12; 2022: 10; 2023: 13; 2024: 10; 2025: 8.

54 papers.

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  1. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  2. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  3. 2021Differentially Private Federated Learning on Heterogeneous Datacited by 74
  4. 2021Consensus Control for Decentralized Deep Learningcited by 49
  5. 2022Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averagingcited by 27
  6. 2023A Survey on Decentralized Federated Learningcited by 27
  7. 2021Decentralized Local Stochastic Extra-Gradient for Variational Inequalitiescited by 25
  8. 2022Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matchingcited by 24
  9. 2020Specialized federated learning using a mixture of expertscited by 23
  10. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  11. 2022Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learningcited by 23
  12. 2022On the (In)security of Peer-to-Peer Decentralized Machine Learningcited by 23
38 more
  1. 2023Personalized Decentralized Federated Learning with Knowledge Distillationcited by 21
  2. 2024The Privacy Power of Correlated Noise in Decentralized Learningcited by 17
  3. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  4. 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graphcited by 11
  5. 2023QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillationcited by 11
  6. 2021Differentially Private Coordinate Descent for Composite Empirical Risk Minimizationcited by 10
  7. 2023Backdoor Attacks in Peer-to-Peer Federated Learningcited by 9
  8. 2021Separation of Powers in Federated Learningcited by 8
  9. 2023Collaborative Learning via Prediction Consensuscited by 8
  10. 2024Privacy Attacks in Decentralized Learningcited by 8
  11. 2024MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classescited by 7
  12. 2022TEE-based decentralized recommender systems: The raw data sharing redemptioncited by 6
  13. 2022Network Gradient Descent Algorithm for Decentralized Federated Learningcited by 5
  14. 2022High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descentcited by 4
  15. 2023Noncommutative C^*-algebra Net: Learning Neural Networks with Powerful Product Structure in C^*-algebracited by 4
  16. 2023Topology-Based Reconstruction Prevention for Decentralised Learningcited by 4
  17. 2023P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setupscited by 3
  18. 2021DeceFL: A Principled Decentralized Federated Learning Frameworkcited by 2
  19. 2021Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descentcited by 2
  20. 2023Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learningcited by 2
  21. 2025Differential Privacy Analysis of Decentralized Gossip Averaging under Varying Threat Modelscited by 2
  22. 2025Unified Privacy Guarantees for Decentralized Learning via Matrix Factorizationcited by 2
  23. 2021A Privacy-Preserving and Trustable Multi-agent Learning Frameworkcited by 1
  24. 2021Preliminary Steps Towards Federated Sentiment Classificationcited by 1
  25. 2023Graph Learning Across Data Siloscited by 1
  26. 2023Multitask Online Learning: Listen to the Neighborhood Buzzcited by 1
  27. 2024Decentralized Personalized Federated Learningcited by 1
  28. 2024Fair Decentralized Learningcited by 1
  29. 2024A Kernel Perspective on Distillation-based Collaborative Learningcited by 1
  30. 2025Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learningcited by 1
  31. 2021Consensus Based Multi-Layer Perceptrons for Edge Computingcited by 0
  32. 2022DeFTA: A Plug-and-Play Decentralized Replacement for FedAvgcited by 0
  33. 2023Resource-constrained knowledge diffusion processes inspired by human peer learningcited by 0
  34. 2024A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniquescited by 0
  35. 2024Quantum Circuit C^*-algebra Netcited by 0
  36. 2024Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learningcited by 0
  37. 2024P4: Towards private, personalized, and Peer-to-Peer learningcited by 0
  38. 2025Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Datacited by 0

50 most cited of 54 All 54 (JSON)