First semi-device independent quantum money scheme introduced, proving unforgeability.
problem Creating secure quantum money without full device independence.
method Inspired by semi-device independent quantum key distribution, relaxes mint's cooperation assumption.
result First unforgeable quantum money scheme with partially relaxed device independence.
Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
problem Improving seizure detection accuracy for wearable devices.
method Transfer learning with tensor kernel machine using canonical polyadic decomposition.
result Patient fine-tuned model achieves high performance with smaller model size.
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.
EdgeAI aims to deploy deep learning on IoT devices.
problem Deep learning's high computational demands on IoT devices.
method Addressing data-independent deployment and communication-aware distributed inference.
result New directions to enable deep learning on IoT devices.
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.
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…
Mobile training improves speech recognition for users with unique speech characteristics.
problem Limited generalization of speaker-independent speech recognition models for users with very different speech characteristics.
method Securely training personalized end-to-end speech recognition models on mobile devices, splitting gradient computation to reduce memory usage.
result On-device personalization achieved 58.1% relative word error rate reduction compared to 63.7% in a server environment, with 18.7% performance degradation.
This paper proposes a new D2D data sharing approach to improve distributed machine learning training speed.
problem Straggler dilemma in distributed edge learning.
method Proposes a D2D data sharing approach to balance computation loads and optimize radio resource allocation.
result Significantly reduces training delay and enhances training accuracy in non-i.i.d. data environments.
PPGnet model estimates heart rate from PPG signals without motion artifacts.
problem Wearable PPG devices struggle with motion artifacts.
method End-to-end deep learning model using 8-second PPG signals.
result Achieved mean absolute error of 3.36+-4.1 BPM on IEEE SPC 2015 dataset.
FedCD improves non-IID federated learning performance.
problem Learning from non-IID data in federated learning.
method FedCD dynamically groups devices with similar data.
result FedCD achieves higher accuracy and faster convergence.
Dream Distillation compresses models without data, achieving high accuracy.
problem Model compression without real data.
method Data-independent model compression framework.
result Achieves 88.5% accuracy on CIFAR-10 test set.
DeepBeat uses deep learning to assess signal quality and detect arrhythmia in wearable devices.
problem Detecting atrial fibrillation from wearable devices with noise.
method Multi-task deep learning approach using convolutional denoising autoencoders.
result Significantly improved AF detection accuracy compared to traditional methods.
This paper optimizes object tracking on edge devices with small matrices.
problem Efficiently tracking objects in video sequences on edge devices with small matrices.
method Parallelized a Simple Online and Real-time Tracking (SORT) application on shared-memory multicores.
result Throughput-based parallelization technique outperforms multi-threading for small matrices.
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 studies communication efficiency in federated learning by optimizing the sum-rate-distortion function for indirect multiterminal source coding.
problem Indirect multiterminal source coding in federated learning where edge devices send noisy gradients to the server.
method Analyzes the rate region for the quadratic vector Gaussian CEO problem under unbiased estimator and derives an explicit formula for the sum-rate-distortion function.
result Derives an explicit formula for the sum-rate-distortion function in the special case of identical gradients over edge devices.
The growing trend of using wearable devices for context-aware computing and pervasive sensing systems has raised its potentials for quick and reliable authentication techniques. Since personal writing habitats differ from each other, it is possible to realize user authentication through writing. This is of great signif…
WEST compresses word embeddings and softmax layers for memory efficiency.
problem Memory constraints in large vocabulary models.
method WEST encodes words with sequences of sub-units, improving compression without performance loss.
result WEST achieves significant compression without sacrificing performance.
New algorithms improve federated learning accuracy and stability with real-world data.
problem Real-world data diversity and imbalance challenge federated learning.
method Developed new algorithms (FedVC, FedIR) to resample and reweight data.
result Significant improvements in accuracy and stability of federated learning.
Anonymizes sensor data to protect user privacy.
problem Protecting user privacy from motion sensor data.
method Information-theoretic approach and multi-objective loss function for deep autoencoders.
result Anonymized sensor data preserves activity recognition accuracy above 92% while keeping user identification accuracy below 7%.
Train one network for efficient deployment across many devices.
problem Efficient inference across diverse devices with minimal resource constraints.
method Once-for-All (OFA) network training and progressive shrinking algorithm.
result OFA network achieves state-of-the-art accuracy with significantly reduced training time and resource usage.
The paper addresses challenges in edge deep learning for IoT, proposing new directions.
problem Challenges in large-scale deep learning adoption for IoT devices.
method Unified view targeting three research directions: federated learning, data-independent deployment, and communication-aware inference.
result A network-centric approach is needed for edge intelligence.
IST trains neural networks locally, reducing memory and communication costs.
problem Challenges in distributed learning due to mandatory data separation.
method Independent subnet training (IST) decomposes the network into narrow subnetworks.
result IST reduces training times compared to common distributed learning approaches.
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.
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.
A framework for large-scale federated learning with non-IID data.
problem Stability of models trained on non-IID data in federated learning.
method Generation of non-IID datasets and modular evaluation framework.
result Open-source benchmark for large-scale federated learning research.
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.
More frequent model updates in FL increase generalization error.
problem Negative impact of frequent communication on FL model generalization.
method Analyzed the effect of the number of rounds of model aggregation on generalization error.
result Generalization error increases with more frequent model updates.
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.
Hand-held system translates foreign menus for diet management.
problem Translation ambiguities and context-specific information for diet management.
method Portable multimedia device, machine translation, context-specific corpora, pre-processing steps, multimedia information.
result Higher accuracy and instant translations compared to Google Translate.
EigenDamage reduces neural network size and FLOPs with structured pruning in the Kronecker-Factored Eigenbasis.
problem Reducing neural network size and FLOPs while maintaining accuracy for resource-constrained devices.
method Kronecker-Factored Eigenbasis reparameterization and Hessian-based structured pruning.
result Empirically validated improvements in model size and FLOPs with negligible accuracy loss.
Survey on-device ML challenges and future directions.
problem Training machine learning models on-device with limited resources.
method Reformulated as resource constrained learning, comparing techniques from various AI areas.
result Identification of open challenges and future research directions.
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.
SpeakerStew verifies 46 languages with reduced training and inference costs.
problem Speaker verification for 46 languages with smart speaker interactions.
method Pooling multilingual data, triage between text-dependent and text-independent models.
result Training on multiple languages generalizes well and reduces computational requirements.
BEGIN network models binary data without parametric assumptions.
problem Conditional independence in non-parametric families of binary data.
method BEGIN network models binary data using sparse linear representations and block factorizations.
result BEGIN network captures conditional independence for arbitrary binary and multinomial variables.
A real-time context-aware system for IoT using mobile devices.
problem Challenges in running machine learning on mobile devices.
method Developed a context-learning algorithm for mobile devices that updates itself periodically from the server.
result Achieved mean accuracy of 97.51% with only 11ms execution time.
Paper optimizes neural architectures for multiple device constraints.
problem NAS ignores device constraints like latency and energy.
method Developed MONAS and DPP-Net for multi-objective optimization.
result Found Pareto-optimal architectures for various devices.
The paper optimizes neural network inference on mobile GPUs.
problem Limited computing power and thermal constraints on mobile CPUs.
method Leverage mobile GPUs for neural network inference.
result Real-time inference of deep neural networks on Android and iOS devices.
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.
This paper analyzes mobile device training of deep learning models.
problem Performance characterization of training deep learning models on mobile devices.
method Experiments on NVIDIA TX2, benchmark suite, and tools for performance analysis.
result Interesting performance problems and opportunities revealed.
Study proposes BFEL framework for privacy-preserving FL in personalized healthcare.
problem Privacy and security concerns in traditional cloud-centric ML, especially in wearable devices.
method Develops a blockchain-enhanced federated edge learning (BFEL) framework based on FedCurv, incorporating fisher information matrix and public key encryption.
result Significant reduction in communication cost and high efficiency for federated training on non-iid and heterogeneous data.
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.
FD and FAug reduce communication in on-device ML with non-IID data.
problem Minimize communication overhead in on-device ML with non-IID data.
method Federated distillation (FD) and federated augmentation (FAug).
result FD with FAug reduces communication by 26x while maintaining high accuracy.
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.