A hybrid neural network optimizes AI deployment on edge and cloud for energy efficiency.
problem Energy and resource constraints in edge devices for deep learning models.
method Conditionally deep hybrid neural network with quantized layers at edge and full-precision layers at cloud.
result Early classification at the edge reduces energy consumption by 5.5x on CIFAR-10 dataset.
New method calibrates probabilistic linear solver for online coverage guarantees.
problem Uncertainty in probabilistic linear solver solutions without coverage guarantees.
method Online conformal prediction-PLS (OCP-PLS) method to calibrate uncertainty thresholds.
result Validates online calibration of uncertainty thresholds via online conformal prediction.
Method certifies edge predictions with cloud-level reliability.
problem Ensuring reliability of edge intelligence models.
method Conformal alignment-based cascading mechanism.
result Certifies conditional coverage with user control over risk level.
OL4EL optimizes edge learning on resource-constrained servers.
problem Resource constraints on edge servers hinder effective distributed machine learning.
method Online Learning for EL (OL4EL) framework using budget-limited multi-armed bandit model.
result OL4EL significantly improves learning performance while conserving resources.
Edge filters reduce video data transmission to datacenters.
problem Strain on wide area network infrastructure due to video camera deployments.
method FilterForward system with lightweight edge filters and microclassifiers.
result Reduces bandwidth use by an order of magnitude.
Adaptive anomaly detection for IoT data reduces delay by 84%.
problem Real-time anomaly detection for IoT devices with limited resources.
method Hierarchical edge computing with adaptive anomaly detection models.
result Reduces detection delay by 84% while maintaining accuracy.
TSDS framework reduces edge LLM agent compute by 43%-73% while maintaining safety and reliability.
problem Managing reasoning budget and uncertainty in edge LLM agents.
method Integrates a lightweight convergence probe and a perplexity-based deferral rule calibrated via multi-objective LTT.
result Reduces per-episode thinking compute by 43%-73% over deferral-only baselines.
STORM enables edge computing for empirical risk minimization.
problem Training models on edge devices for streaming data.
method Online sketching for empirical risk minimization.
result STORM can estimate least-squares objective accurately.
Paper presents privacy-preserving techniques for HD computing.
problem Privacy loss in HD computing due to reversible computation.
method Quantization and pruning of hypervectors for differential privacy.
result Differentially private HD model for cloud inference.
Edge devices learn directly from personal data using spiking networks.
problem Processing personal data on edge devices with low latency and energy efficiency.
method Spiking Neural Networks for local training on edge devices.
result Spiking networks enable efficient local training on edge devices without scalability limitations.
Secure asynchronous federated learning with differential privacy for edge intelligence.
problem Privacy concerns in federated learning for edge computing.
method Differential privacy applied to secure asynchronous federated learning.
result MAPA improves model accuracy and convergence speed with sufficient privacy.
New method distills cloud models into edge-friendly ones.
problem Cloud-to-edge model compression with limited data exchange.
method Two-step workflow of deprivatization and distillation.
result Outperforms previous state-of-the-art approaches on various benchmarks.
Edge computing addresses AI on IoT devices by processing data locally.
problem Processing AI on resource-constrained IoT devices is challenging.
method Deploying machine learning systems at the edge of the network.
result Edge computing reduces latency and communication costs.
Paper presents a lightweight, unobtrusive method to protect edge device data privacy.
problem Protecting inference data privacy in IoT edge devices with limited compute power.
method A lightweight neural network at edge devices to obfuscate inference data without indicating obfuscation.
result Effectively protects inference data confidentiality while preserving backend accuracy.
Edge language models show bias over time, especially on resource-constrained devices.
problem Bias in edge language models on resource-constrained devices.
method Comparative analysis of text-based bias across edge, cloud, and desktop environments; optimized Llama-2 model on Raspberry Pi 4; feedback loop mechanism to correct bias.
result Llama-2 on Raspberry Pi 4 shows 43.23% and 21.89% more bias over time compared to cloud and desktop models.
A new framework reduces data upload for image classification while protecting user privacy.
problem Data upload limitations and privacy concerns in cloud-based image classification.
method Unsupervised autoencoder training at edge devices, followed by latent vector transmission to server for classifier training.
result The framework reduces communications overhead and protects user data privacy.
This paper tackles URLLC in 6G networks with deep learning.
problem Stringent requirements on end-to-end delay and reliability for mission-critical applications.
method Develops a multi-level architecture combining theoretical models and real-world data, using deep transfer learning and federated learning.
result Demonstrates improved performance in URLLC for mission-critical applications.
To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing …
DQN optimizes traffic light control policies in ITSs.
problem Challenges in scalable real-time actuation mechanisms for smart traffic management.
method Exploration of Deep Q-Networks (DQN) for traffic light control policies.
result DQN algorithms produce intelligent behavior, such as greenwave patterns.
We consider point clouds obtained as random samples of a measure on a Euclidean domain. A graph representing the point cloud is obtained by assigning weights to edges based on the distance between the points they connect. Our goal is to develop mathematical tools needed to study the consistency, as the number of availa…
Adaptive anomaly detection for IoT data reduces delay without sacrificing accuracy.
problem Real-time anomaly detection for IoT data in distributed edge computing systems.
method Adaptive anomaly detection approach using contextual bandit and reinforcement learning.
result Significantly reduces detection delay (e.g., 71.4% for univariate data) without sacrificing accuracy.
Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As the algorithms become mature and efficient, more and more ML inference is moving out of datacenters/cloud and deployed on edge devices. This…
EI-MTD defends edge intelligence against adversarial attacks with dynamic scheduling.
problem Adversarial attacks on edge intelligence models.
method EI-MTD uses differential knowledge distillation to create robust member models and a dynamic scheduling policy based on a Bayesian Stackelberg game.
result EI-MTD effectively protects edge intelligence from black-box adversarial attacks.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Su…
SLIP secures LLMs on edge devices by splitting computation and protecting sensitive parts.
problem Protecting LLMs on edge devices from theft and unauthorized use.
method SLIP uses matrix decomposition to split model computation between secure and vulnerable resources, ensuring zero accuracy degradation and minimal latency.
result SLIP is the first practical, secure hybrid protocol for protecting LLMs on edge devices.
FedDST trains sparse sub-networks to improve efficiency in federated learning.
problem Efficiently train large models on resource-limited edge devices with limited network bandwidth.
method Dynamic sparse training to reduce model size and communication.
result FedDST outperforms existing methods in federated learning, especially in non-i.i.d. settings.
New method compresses LSTM networks using MPS tensor trains.
problem Challenges in maintaining performance of compressed RNNs.
method Use of MPS tensor trains for LSTM network compression.
result MPS tensor trains outperform MPOs in storage and inference time.
SparseIDS uses RL to significantly reduce packet inspection in IDSs.
problem Efficiently inspecting large volumes of network packets for security threats.
method SparseIDS employs RL to learn optimal packet sampling strategies.
result SparseIDS reduces packet inspection by more than 75% while maintaining high accuracy.
Federated learning on edge devices achieves high accuracy with minimal data exchange.
problem Training deep neural networks on edge devices while maintaining user privacy.
method Training CNN, LSTM, and MLP on MNIST data using federated learning on edge devices (Raspberry Pi4s). Experimentally tested on IID and non-IID samples.
result Up to 85% test accuracy achieved with 2 minutes of training time and <10 MB data exchange per device.
New method reduces cloud usage for mobile/IoT predictions.
problem Minimizing cloud usage for mobile/IoT predictions while maintaining accuracy.
method Formulating the BL problem via bracketing functions h−,h+ from a simple class. result Improved performance over prior methods, validated on real-world datasets.
Framework optimizes cloud container sizing for ML tasks.
problem Challenges in cloud container configuration for ML services.
method Autonomous scaling using nested-loop Monte Carlo simulation.
result Reduces compute cost and accelerates ML algorithms.
In recent decades, the use of 3D point clouds has been widespread in computer industry. The development of techniques in analyzing point clouds is increasingly important. In particular, mapping of point clouds has been a challenging problem. In this paper, we develop a discrete analogue of the Teichmüller extremal mapp…
Shredder reduces inference privacy by adding noise to data without significantly affecting accuracy.
problem Protecting privacy of private and privileged data sent to cloud servers for inference.
method Develops Shredder, an end-to-end framework that learns additive noise distributions to reduce data information content.
result Reduces mutual information between input and communicated data by 74.70% while maintaining 1.58% accuracy loss.
A new Helmholtzian operator from point clouds for flow analysis.
problem Analyzing flows and vector fields on manifolds from point cloud data.
method Estimation of manifold Helmholtzian from point cloud data using weighted 1-Laplacian.
result The Helmholtzian operator L1 effectively smooths, predicts, and extracts features from flows on manifolds. Flower framework simplifies federated learning experiments on edge devices.
problem Realistic implementation of Federated Learning on edge devices is challenging.
method Developed a comprehensive federated learning framework, Flower, supporting large-scale experiments on heterogeneous devices.
result Flower enables federated learning experiments with up to 15M client size using only two high-end GPUs.
Method computes harmonic and conformal maps from point clouds.
problem Computing maps from irregular point cloud data.
method Meshless method using cubic lattice approximations.
result Harmonic and conformal maps computed accurately.
TRA detects causal direction from bivariate data using geometric shapes.
problem Inferring causal direction from observational data is challenging and unreliable.
method TRA compares rank-based copula-standardized residual clouds to detect causal direction.
result TRA is robust and superior in detecting causal direction across various scenarios.
Paper proposes a hierarchical approach to malware detection in cloud environments.
problem Malware threat in cloud computing environments.
method Machine learning on graphs, hypergraphs, and natural language for malware detection and analysis.
result Federated learning for malware detection in multicloud environments.
SPINN optimizes neural network inference on devices and cloud.
problem Inference on mobile devices is challenging due to high computational demands and dynamic connectivity.
method Synergistic progressive inference with a novel scheduler.
result SPINN achieves up to 2x higher throughput and reduces server cost by up to 6.8x.
Develops a method for conformal parameterization of point clouds without fixed boundaries.
problem Desirable distortion in fixed-boundary parameterizations of point clouds.
method Free-boundary conformal parameterization method involving approximation of point cloud Laplacian and boundary treatment.
result High-quality point cloud meshing achieved through the proposed method.
Paper presents a TL approach to reduce drone training time and energy consumption.
problem Limited power and compute capability of edge drones.
method Transfer Learning for deep neural network training via Deep Reinforcement Learning.
result 3.7x reduction in energy consumption and 1.8x reduction in training latency.
CHOPT optimizes hyperparameters in cloud ML platforms efficiently.
problem Efficient hyperparameter optimization in cloud-based machine learning platforms.
method Cloud-based HyperOpt framework with web interfaces and analysis tools.
result CHOPT finds hyperparameter configurations competitive with previous work.
RL enhances cloud autoscaling by learning optimal resource allocation.
problem Optimizing resource allocation in dynamic, uncertain cloud environments.
method Reinforcement Learning for dynamic, transparent, and adaptable resource management policies.
result RL enables transparent, dynamic, and adaptable resource management policies.
ShapeVis visualizes high-dimensional data efficiently.
problem Scalability issues with Mapper in high-dimensional data.
method Landmarks, weighted witness-graph, induced maps, modularity-based pruning.
result ShapeVis scales to millions of data points while preserving visualization quality.
A novel approach for federated learning over-the-air computation to reduce latency and improve privacy.
problem Low-latency and privacy issues in edge machine learning for intelligent devices.
method Over-the-air computation and sparse-low-rank optimization for efficient global model aggregation.
result Efficient global model aggregation with low latency and improved privacy.
Predicts and classifies computational jobs for efficient resource allocation in cloud centers.
problem Efficiently scheduling and assigning resources to computational jobs in cloud centers.
method Applied LSTM neural network for job arrival prediction and BIRCH clustering for job classification.
result Improved accuracy in predicting and classifying computational jobs compared to existing methods.
Serverless cloud computing speeds up double machine learning model estimation.
problem Efficiently estimating double machine learning models with minimal cloud resource management.
method Serverless computing with AWS Lambda for repeated cross-fitting.
result Demonstrates significant reduction in estimation times and costs.
Point cloud is the most fundamental representation of 3D geometric objects. Analyzing and processing point cloud surfaces is important in computer graphics and computer vision. However, most of the existing algorithms for surface analysis require connectivity information. Therefore, it is desirable to develop a mesh st…