# Decentralized Collaborative Learning with Probabilistic Data Protection

arXiv: https://arxiv.org/abs/2208.10674 · HTML: https://mlsanity.com/paper/2208.10674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 23 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [cpSGD: Communication-efficient and differentially-private distributed SGD](https://mlsanity.com/paper/1805.10559/) (2018)
- [Differentially Private Bayesian Inference for Exponential Families](https://mlsanity.com/paper/1809.02188/) (2018)
- [A Survey of Distributed Consensus Protocols for Blockchain Networks](https://mlsanity.com/paper/1904.04098/) (2019)
- [Differentially Private Bayesian Learning on Distributed Data](https://mlsanity.com/paper/1703.01106/) (2017)
- [Secure and Privacy-Preserving Consensus](https://mlsanity.com/paper/1707.04491/) (2017)
- [Comparing Population Means under Local Differential Privacy: with Significance and Power](https://mlsanity.com/paper/1803.09027/) (2018)

## Built on by (most cited first, 1 of 1)

- [Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis](https://mlsanity.com/paper/2404.01270/) (2024)

## Abstract (arXiv)

We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network participants can gain extra insights that cannot be accessed when disconnected from the others. As such, we propose a decentralized machine learning framework that is carefully designed to respect the values of democracy, diversity, and privacy. Specifically, we propose a federated multi-task learning framework that integrates a privacy-preserving dynamic consensus algorithm. We show that a specific network topology called the expander graph dramatically improves the scalability of global consensus building. We conclude the paper by making some remarks on open problems.