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Found 13 papers, 2 papers with code
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning
Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry.
Generative Model-based Feature Knowledge Distillation for Action Recognition
Addressing this gap, our paper introduces an innovative knowledge distillation framework, with the generative model for training a lightweight student model.
Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling
To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics.
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally.
ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence
Intelligent applications based on machine learning are impacting many parts of our lives.
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data.
Eigenvalues of the Laplace operator with potential under the backward Ricci flow on locally homogeneous 3-manifolds
Let $\lambda(t)$ be the first eigenvalue of $-\Delta+aR\, (a>0)$ under the backward Ricci flow on locally homogeneous 3-manifolds, where $R$ is the scalar curvature.
Differential Geometry 53E20, 58C40
Latent Dirichlet Allocation Model Training with Differential Privacy
To address the privacy issue in LDA, we systematically investigate the privacy protection of the main-stream LDA training algorithm based on Collapsed Gibbs Sampling (CGS) and propose several differentially private LDA algorithms for typical training scenarios.
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning
The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation.
OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints
However, heterogeneous and limited computation and communication resources on edge servers (or edges) pose great challenges on distributed ML and formulate a new paradigm of Edge Learning (i. e. edge-cloud collaborative machine learning).
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate.
Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis
Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data.
On Privacy Protection of Latent Dirichlet Allocation Model Training
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications.
