# Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis

arXiv: https://arxiv.org/abs/2404.01270 · HTML: https://mlsanity.com/paper/2404.01270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability](https://mlsanity.com/paper/1703.06182/) (2017)
- [Posterior Collapse and Latent Variable Non-identifiability](https://mlsanity.com/paper/2301.00537/) (2023)
- [The Usual Suspects? Reassessing Blame for VAE Posterior Collapse](https://mlsanity.com/paper/1912.10702/) (2019)
- [A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning](https://mlsanity.com/paper/2006.04338/) (2020)
- [Federated Variational Learning for Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2108.08404/) (2021)
- [Decentralized Collaborative Learning with Probabilistic Data Protection](https://mlsanity.com/paper/2208.10674/) (2022)

## Abstract (arXiv)

This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generation Blockchain platforms. First, we expand the existing framework for collaborative dictionary learning (CollabDict), which has previously been limited to Gaussian mixture models, by incorporating deep variational autoencoders (VAEs) into the framework, with a particular focus on anomaly detection. We demonstrate that the VAE-based anomaly score function shares the same mathematical structure as the non-deep model, and provide comprehensive qualitative comparison. Second, considering the widespread use of "pre-trained models," we provide a mathematical analysis on data privacy leakage when models trained with CollabDict are shared externally. We show that the CollabDict approach, when applied to Gaussian mixtures, adheres to a Renyi differential privacy criterion. Additionally, we propose a practical metric for monitoring internal privacy breaches during the learning process.