Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionDictionary LearningMulti-Task LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses
Federated Learning is a privacy preserving decentralized machine learning paradigm designed to collaboratively train models across multiple clients by exchanging gradients to the server and keeping private data local. Ne…
Federated LearningPrivacy PreservingFedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning
Federated Learning (FL) enables collaborative training of models across distributed clients without sharing local data, addressing privacy concerns in decentralized systems. However, the gradient-sharing process exposes …
Federated LearningPrivacy PreservingSplit-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation
Split learning (SL) has been proposed to train deep learning models in a decentralized manner. For decentralized healthcare applications with vertical data partitioning, SL can be beneficial as it allows institutes with …
Brain Tumor SegmentationImage SegmentationSegmentationSemantic Segmentation+1Privacy Preserving Semi-Decentralized Mean Estimation over Intermittently-Connected Networks
We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fail intermittently. We adopt a semi-decent…
Privacy PreservingMitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacy
Imagine a group of citizens willing to collectively contribute their personal data for the common good to produce socially useful information, resulting from data analytics or machine learning computations. Sharing raw p…