# Personalized and Private Peer-to-Peer Machine Learning

arXiv: https://arxiv.org/abs/1705.08435 · HTML: https://mlsanity.com/paper/1705.08435/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 30 of 54)

- [Federated Multi-Task Learning under a Mixture of Distributions](https://mlsanity.com/paper/2108.10252/) (2021)
- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Differentially Private Federated Learning on Heterogeneous Data](https://mlsanity.com/paper/2111.09278/) (2021)
- [Consensus Control for Decentralized Deep Learning](https://mlsanity.com/paper/2102.04828/) (2021)
- [Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging](https://mlsanity.com/paper/2206.05091/) (2022)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Decentralized Local Stochastic Extra-Gradient for Variational Inequalities](https://mlsanity.com/paper/2106.08315/) (2021)
- [Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching](https://mlsanity.com/paper/2203.12285/) (2022)
- [Specialized federated learning using a mixture of experts](https://mlsanity.com/paper/2010.02056/) (2020)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning](https://mlsanity.com/paper/2202.05318/) (2022)
- [On the (In)security of Peer-to-Peer Decentralized Machine Learning](https://mlsanity.com/paper/2205.08443/) (2022)
- [Personalized Decentralized Federated Learning with Knowledge Distillation](https://mlsanity.com/paper/2302.12156/) (2023)
- [The Privacy Power of Correlated Noise in Decentralized Learning](https://mlsanity.com/paper/2405.01031/) (2024)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph](https://mlsanity.com/paper/2210.00325/) (2022)
- [QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation](https://mlsanity.com/paper/2311.15603/) (2023)
- [Differentially Private Coordinate Descent for Composite Empirical Risk Minimization](https://mlsanity.com/paper/2110.11688/) (2021)
- [Backdoor Attacks in Peer-to-Peer Federated Learning](https://mlsanity.com/paper/2301.09732/) (2023)
- [Separation of Powers in Federated Learning](https://mlsanity.com/paper/2105.09400/) (2021)
- [Collaborative Learning via Prediction Consensus](https://mlsanity.com/paper/2305.18497/) (2023)
- [Privacy Attacks in Decentralized Learning](https://mlsanity.com/paper/2402.10001/) (2024)
- [MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes](https://mlsanity.com/paper/2404.09232/) (2024)
- [TEE-based decentralized recommender systems: The raw data sharing redemption](https://mlsanity.com/paper/2202.11655/) (2022)
- [Network Gradient Descent Algorithm for Decentralized Federated Learning](https://mlsanity.com/paper/2205.08364/) (2022)
- [High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent](https://mlsanity.com/paper/2207.01560/) (2022)
- [Noncommutative C^*-algebra Net: Learning Neural Networks with Powerful Product Structure in C^*-algebra](https://mlsanity.com/paper/2302.01191/) (2023)
- [Topology-Based Reconstruction Prevention for Decentralised Learning](https://mlsanity.com/paper/2312.05248/) (2023)
- [P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups](https://mlsanity.com/paper/2302.13438/) (2023)
- [Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning](https://mlsanity.com/paper/2309.14139/) (2023)

## 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.