This paper reviews quantum machine learning from NISQ to fault tolerance.
problem The challenges and opportunities in quantum machine learning.
method Comprehensive review of quantum machine learning concepts.
result Coverage of NISQ and fault-tolerant quantum computing approaches.
Meta-learning algorithms prepare quantum Gibbs states efficiently for NISQ devices.
problem Efficiently preparing quantum Gibbs states for NISQ devices.
method Meta-Variational Quantum Thermalizer (Meta-VQT) and Neural Network Meta-VQT (NN-Meta VQT) algorithms.
result Meta-learned parameters significantly outperform random initializations in optimization tasks.
The paper explores the generalization of quantum neural networks using stability theory.
problem Understanding the generalization properties of quantum neural networks.
method The authors use algorithmic stability to establish generalization bounds for quantum neural networks.
result The paper provides practical insights into the design and training of quantum neural networks.
Evolutionary strategy optimizes quantum circuit design and parameters.
problem Optimizing quantum circuit design and parameters for NISQ devices.
method Simple evolutionary strategy to optimize both circuit architecture and parameters.
result Minor slowdown on actual quantum hardware compared to simulations, with insights into mutation operations.
Photonic quantum reinforcement learning for control problems.
problem Solving continuous control problems with noisy quantum computers.
method Proximal policy optimization for photonic variational quantum agents.
result Photonic policy learning achieves comparable performance to classical neural networks.
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. How…
Quantum algorithms for CVaR portfolio optimization face trade-offs between hardware coherence and expressibility.
problem Quantum algorithmic resilience for CVaR portfolio optimization
method WS-QAOA vs. HE-VQNN
result WS-QAOA provides exact theoretical mapping but suffers from hardware decoherence, while HE-VQNN preserves hardware coherence but lacks expressibility.
InfoQGAN uses mutual information to improve QGANs, overcoming mode collapse and feature disentanglement issues.
problem Mode collapse and lack of feature control in QGANs.
method Integrates InfoGAN principles with variational quantum circuit, classical discriminator, and MINE for mutual information optimization.
result InfoQGAN effectively mitigates mode collapse and achieves robust feature disentanglement.
Study finds no significant difference in neural network weights with quantum random numbers.
problem Effects of biased quantum random numbers on neural network initialization.
method Empirical study using quantum hardware and classical pseudo-random numbers.
result No statistically significant difference found between quantum random numbers and other types.
Researchers develop a framework for quantum machine learning models.
problem Comparing quantum machine learning models and their resource requirements.
method Constructive framework of linear quantum models using quantum information theory.
result Linear quantum models require exponentially more qubits than data re-uploading models for certain learning tasks.
Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural networks architecture selection. In this work, we propose an improved…
This work proposes efficient classical training protocols for IQP circuits to train quantum generative models.
problem Training quantum generative models on industrially relevant probability distributions is challenging due to high computational cost.
method Developed protocols for classical training of IQP circuits, which are hard to sample but have efficient gradient computation.
result Classically trained IQP circuits can efficiently sample from target probability distributions, demonstrating practical quantum advantage.
FedZKT enables resource-constrained devices to participate in federated learning with heterogeneous models.
problem Inequality in resource allocation hinders participation from resource-constrained devices in federated learning.
method Zero-shot knowledge transfer through a server-assigned distillation process.
result FedZKT effectively transfers knowledge across heterogeneous on-device models without requiring comparable local training efforts.
Deep learning identifies unknown IoT devices in network traffic.
problem Unauthorized IoT devices pose security risks in BYOD environments.
method Deep learning applied to network traffic images for IoT device identification.
result Over 99% accuracy in identifying 10 IoT devices and non-white-listed devices.
On-device federated learning updates edge models by exchanging trained results.
problem Limited training data at edge devices due to model drift.
method OS-ELM for sequential training and autoencoder for anomaly detection, combined with federated learning.
result The proposed approach produces a merged model as accurately as traditional methods with lower costs.
A method for trust evaluation of devices in human-device coexistence systems.
problem Efficient trust evaluation of devices in systems with diverse physical and social attributes.
method Canonical correlation analysis-enhanced hypergraph self-supervised learning (HSLCCA).
result The proposed HSLCCA method significantly outperforms baseline algorithms in identifying trusted devices.
Two approaches scale up DNN optimization for diverse edge devices.
problem Optimizing DNNs for edge devices with varying performance requirements.
method Reuse performance predictors on proxy devices and build scalable predictors.
result Optimized DNN designs for many different edge devices without lengthy optimization.
This paper optimizes how deep learning models are distributed across different devices.
problem Optimizing how large, complex neural networks are split across multiple devices.
method Identified and solved an optimization problem for device placement of DNN operators.
result Automated algorithms that solve the device placement problem for modern pipelined settings.
Distributed learning adapts to diverse devices, improving performance.
problem Training neural networks on devices with varying capabilities and resources.
method Each device trains a customized neural network, sharing parameters with others.
result Achieves higher rewards on more powerful devices without sacrificing weaker ones.
Flexible device participation improves federated learning convergence.
problem Strict device participation limits federated learning reach.
method Analytical results and new aggregation scheme for flexible participation.
result Convergence improved with flexible device participation.
The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in i…
Semi-decentralized federated learning combines device-to-server and device-to-device communications for faster convergence.
problem Faster convergence in federated learning with decentralized model training.
method Two timescale hybrid federated learning (TT-HF) with cooperative D2D model aggregations.
result Achieves sublinear convergence rate of O(1/t) with adaptive control algorithm.
This paper optimizes AI inference on edge devices with reduced communication and computation costs.
problem Efficiently performing AI inference on resource-constrained edge devices with reduced communication and computation costs.
method A three-step framework for effective inference: model split point selection, communication-aware model compression, and task-oriented encoding of intermediate features.
result Our proposed framework achieves a better trade-off and significantly reduces inference latency compared to baseline methods.
AutoML has become a popular service that is provided by most leading cloud service providers today. In this paper, we focus on the AutoML problem from the \emph{service provider's perspective}, motivated by the following practical consideration: When an AutoML service needs to serve {\em multiple users} with {\em multi…
We propose a real-time context-aware learning system along with the architecture that runs on the mobile devices, provide services to the user and manage the IoT devices. In this system, an application running on mobile devices collected data from the sensors, learned about the user-defined context, made predictions in…
SplitEasy trains ML models on mobile devices without server data transfer.
problem Training complex DL models on resource-limited mobile devices.
method Split learning approach where sensitive layers are trained locally, computationally intensive layers on server.
result SplitEasy trains models on mobile devices with minimal data transfer, near-constant time per sample.
Fog learning distributes ML model training across heterogeneous devices and networks.
problem Challenges with conventional federated learning in heterogeneous networks.
method Intelligent distribution of ML model training across nodes from edge devices to cloud servers.
result Enhanced federated learning with multi-layer hybrid framework considering network, heterogeneity, and proximity.
FedCluster accelerates federated learning convergence by cycling device groups.
problem Federated learning convergence issues with device-level data heterogeneity.
method FedCluster groups devices into clusters that cycle through learning rounds, boosting convergence with meta-updates.
result FedCluster achieves faster convergence in nonconvex optimization compared to FedAvg.
A new federated learning framework for handling device heterogeneity.
problem Handling device heterogeneity in federated learning.
method Superquantile-based objective with parameterized levels of conformity, optimized using secure aggregation.
result The optimization algorithm converges to a stationary point.
SEFR is a fast, energy-efficient classifier for ultra-low power devices.
problem Running machine learning on battery-powered devices is challenging due to time and energy constraints.
method SEFR is an ultra-low power classifier with linear time complexity for training and testing.
result SEFR is 63 times faster and 70 times more energy efficient than state-of-the-art classifiers.
Automates detection of electric devices in 3D x-ray images of luggage.
problem Detecting electric devices in cluttered 3D baggage images.
method Unpack, Predict, eXtract, Repack (UXPR) algorithm using segmentation and ensemble learning.
result System can accurately detect electric devices in 3D baggage images.
A lightweight FPGA-based reinforcement learning approach for edge devices.
problem Resource constraints and inefficiency of DQN on edge devices.
method OS-ELM based training algorithm and L2 regularization for stability.
result 29.77x and 89.40x faster than conventional DQN-based approach for CartPole-v0 task.
AMS improves video inference on edge devices by adapting a small model with online knowledge distillation.
problem High computation cost of Deep Neural Networks for real-time video inference on edge devices.
method AMS uses a remote server to continually train and adapt a small model on edge devices, using online knowledge distillation from a large model.
result 0.4--17.8 percent mean Intersection-over-Union improvement in video semantic segmentation.
Federated learning enables private model training across devices.
problem Private and collaborative machine learning across multiple devices.
method Designing scalable, privacy-preserving FL systems using graph-based optimization.
result Personalized models for each device while maintaining data privacy.
Recurrent neural networks (RNNs) are powerful constructs capable of modeling complex systems, up to and including Turing Machines. However, learning such complex models from finite training sets can be difficult. In this paper we empirically show that RNNs can learn models of computer peripheral devices through input a…
Recent breakthroughs in Neural Architectural Search (NAS) have achieved state-of-the-art performance in many tasks such as image classification and language understanding. However, most existing works only optimize for model accuracy and largely ignore other important factors imposed by the underlying hardware and devi…
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.
POET enables large neural network training on tiny devices with reduced energy.
problem Training large neural networks on memory-limited edge devices.
method Jointly optimizes rematerialization and paging for memory reduction, formulating an MILP for energy-efficient training.
result POET trains ResNet-18 and BERT within Cortex-M memory constraints, outperforming current methods in energy efficiency.
On-device inference of machine learning models for mobile phones is desirable due to its lower latency and increased privacy. Running such a compute-intensive task solely on the mobile CPU, however, can be difficult due to limited computing power, thermal constraints, and energy consumption. App developers and research…
Survey of knowledge distillation for resource-limited devices.
problem Deploying large deep learning models on resource-limited devices.
method Knowledge distillation using a smaller model trained with information from a larger model.
result A new metric (distillation metric) for comparing different knowledge distillation algorithms.
MetaDVFS uses device and application metadata to improve DVFS efficiency.
problem Improving energy efficiency in mobile platforms with diverse applications and hardware.
method Formulates DVFS as a multi-task reinforcement learning problem and introduces MetaDVFS, leveraging metadata for knowledge transfer.
result MetaDVFS achieves up to 26% improvement in Quality of Experience and up to 17% improvement in Performance-Power Ratio.
Training deep learning models on mobile devices recently becomes possible, because of increasing computation power on mobile hardware and the advantages of enabling high user experiences. Most of the existing work on machine learning at mobile devices is focused on the inference of deep learning models (particularly co…
DarkneTZ protects edge devices from DNN model leaks using TEE and model partitioning.
problem Privacy risks of pre-trained DNNs on edge devices through membership inference attacks.
method Model partitioning into sensitive and untrusted parts, leveraging TEE.
result DarkneTZ provides reliable model privacy with minimal performance overhead.
Federated learning is a method of training models on private data distributed over multiple devices. To keep device data private, the global model is trained by only communicating parameters and updates which poses scalability challenges for large models. To this end, we propose a new federated learning algorithm that …
Paper proposes an AutoML framework for efficient device-edge co-inference.
problem Finding optimal hyper-parameters for model sparsity and feature compression.
method Sequential decision problem solved using deep reinforcement learning (DRL).
result Achieves better communication-computation trade-off and significant speedup.
Paper proposes a human-algorithm approach to reduce medical device recall risk and workload.
problem High recall rate and regulatory workload in FDA's 510(k) pathway.
method Developed machine learning models to estimate recall risk and proposed a data-driven clearance policy.
result Conservative evaluation of policy shows a 32.9% improvement in recall rate and 40.5% reduction in workload.
This study improves quantum classifiers by optimizing data preprocessing.
problem Quantum Machine Learning advantages are not yet clearly demonstrated.
method Used Linear Discriminant Analysis (LDA) for data preprocessing.
result Variational Quantum Algorithm (VQA) outperforms classical classifiers.
New algorithm extracts device profiles for short-term power predictions in commercial buildings.
problem Short-term power prediction in commercial buildings with high accuracy.
method Unsupervised extraction of device profiles from aggregate power measurements, disaggregation using particle swarm optimization, and state changes forecast by artificial neural networks.
result Developed approach outperforms existing methods with high accuracy.