This paper optimizes mobile device computation offloading using deep reinforcement learning.
problem Optimizing computation offloading for mobile devices in virtual edge computing systems.
method Modeling the problem as a Markov decision process and using double deep Q-networks for learning optimal offloading policies.
result The proposed algorithms significantly improve computation offloading performance.
Paper proposes an algorithm to minimize long-term latency in task offloading.
problem Minimizing long-term latency in task offloading.
method Formulated a stochastic programming problem and proposed an algorithm for non-stationary bandit model.
result Proved asymptotic optimality of the proposed algorithm in a non-stationary fog-enabled network.
A DRL approach optimizes computation offloading in MEC systems for mobile users.
problem Optimizing computation offloading in MEC systems with mobile users and stochastic task arrivals.
method Deep Deterministic Policy Gradient (DDPG) for decentralized dynamic computation offloading.
result The DDPG-based strategy outperforms conventional strategies in terms of computation cost and power-delay tradeoff.
Optimizes task offloading in fog networks with limited feedback.
problem Optimizing task offloading in fog networks with limited feedback.
method Multi-armed bandit framework and UCB-type algorithm.
result Maximizes long-term happiness metric in task offloading.
Privacy-preserving offloading for deep learning on IoT devices.
problem Resource constraints and privacy risks in deep neural network computations on IoT devices.
method Obfuscation of data before offloading to a remote node, allowing accurate classification by trained neural networks.
result Data obfuscation enables accurate neural network classification without exposing sensitive information.
Julia programs and ML models are compiled and offloaded to Google Cloud TPUs.
problem Efficiently compiling and executing machine learning models on Google Cloud TPUs.
method Developed a method to compile and offload Julia programs and ML models to Google Cloud TPUs using the Google XLA compiler.
result Successfully fused a VGG19 model into a single TPU executable, achieving 0.23s evaluation time on a batch of 100 images.
This paper tackles task offloading in edge computing systems with dynamic interactions.
problem Dynamic interactions among mobile users in an edge computing system.
method Modelled as a non-cooperative stochastic game, transformed into a Markov decision process, and solved using online deep reinforcement learning.
result The proposed deep RL scheme balances age of information and energy consumption.
Paper uses machine learning to optimize UAV deployment for traffic offloading.
problem Optimizing UAV deployment for efficient traffic offloading from ground BSs.
method LSTM for traffic prediction, KEG algorithm for service area determination, multi-access techniques comparison.
result RSMA reduces up to 24% total power consumption compared to conventional methods.
Bayesian method predicts runtime metrics for fog manufacturing.
problem Accurate prediction of runtime performance metrics in fog manufacturing.
method Bayesian sparse regression for multivariate mixed responses.
result Enhanced prediction and statistical inferences of runtime metrics.
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.
Deep learning approach for efficient IoT task scheduling in MEC networks.
problem Minimizing task latency in IoT users with large-scale MEC systems.
method Stacked auto-encoder for data compression, adaptive simulated annealing, experience replay.
result Near-optimal performance with significantly reduced computational time.
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.
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.
Uniform convergence of isotopies implies ambient isotopy, aiding knot equivalence.
problem Determining when uniform convergence of isotopies leads to ambient isotopies.
method Using a diagrammatic condition to offload uniform convergence, constructing examples of tame knots.
result Constructing tame knots with countably-many crossings, distinguishing them from wild curves.
This paper tackles efficient resource control in IoT edge computing using deep reinforcement learning.
problem Efficient allocation and scheduling of limited resources in IoT edge computing systems.
method Formulated as a CTMDP model, used deep reinforcement learning (RL) to approximate value functions and solve the MDP problem.
result Significant performance improvement over baseline algorithms and RL algorithms based on other architectures.
Modeling dealer competition, internalisation and externalisation impact market dynamics and costs.
problem Understanding and optimizing dealer competition in risky asset markets.
method Variational approach to derive Nash equilibrium for dealer competition.
result Dealers using internalisation are incentivized to increase externalisation, leading to higher hedging costs and wider spreads.
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.
Hierarchical FL reduces latency in HCNs by sharing model updates.
problem Latency and privacy issues in federated learning across heterogeneous cellular networks.
method Hierarchical federated learning, gradient sparsification, periodic averaging.
result Significant reduction in communication latency without compromising model accuracy.
In this paper, we address the problem of reconstructing coverage maps from path-loss measurements in cellular networks. We propose and evaluate two kernel-based adaptive online algorithms as an alternative to typical offline methods. The proposed algorithms are application-tailored extensions of powerful iterative meth…
This work shows how to efficiently simulate parts of quantum landscapes using classical computers.
problem Identifying where quantum computers are advantageous and offloading computations.
method Developed a quantum-enhanced classical algorithm to simulate sub-regions of quantum landscapes.
result It is possible to generate a classical surrogate of a sub-region of a quantum landscape.
A method for disentangling discrete and continuous factors of data without using a discriminator network.
problem Unsupervised disentanglement of discrete and continuous factors of data.
method A procedure that minimizes total correlation of continuous latent variables and a separate discrete inference procedure.
result The method significantly outperforms current disentanglement methods based on disentanglement score and inference network classification score.
Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.
problem Strategic interactions and information leakage in broker-mediated markets.
method Study of strategic trading behavior and information leakage in a broker-mediated market.
result Brokers hold a strategic advantage over informed traders due to information leakage in trading flows.
Optimizes pipelined computation and communication for edge learning within latency constraints.
problem Balancing data transmission and model training to meet latency requirements.
method Analyzes the optimal packet payload size tradeoff between bias and variance.
result Derives analytical bounds on the expected optimality gap for effective optimization.
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.
Study Nash equilibrium between broker and informed trader in dealer and lit markets.
problem Nash equilibrium between broker and informed trader in dealer and lit markets with partial information.
method Convex analysis, FBSDEs, polynomial approximation.
result Existence and uniqueness of Nash equilibrium for short time horizons.
BBCI uses meta prediction to estimate causal effects from datasets.
problem Estimating causal effects from observed data.
method Meta prediction to learn causal effect estimation.
result BBCI accurately estimates ATEs and CATEs across various causal inference problems.
Paper introduces HISA for efficient FHE computations.
problem Efficiently evaluating encrypted neural networks.
method Developed HISA for FHE applications, including compiler and runtime.
result Generated code is faster than hand-optimized implementations.
New algorithms secure IoT edge computing from jamming attacks.
problem Secure mobile edge computing in IoT under jamming attacks.
method Online learning tools for developing SAVE-S and SAVE-A algorithms.
result Achieves sublinear regret without extra resources.
This research optimizes handover between fog nodes in vehicular IoT using machine learning.
problem Smooth transition of device connections and offloaded tasks between fog nodes in vehicular IoT.
method Proposes a three-layer feed-forward neural network and a dual stacked RNN with LSTM cells to predict fog nodes and minimize service interruption.
result Achieved 99.2% accuracy in predicting fog nodes with a test set.
Proposes a new TS algorithm for non-stationary bandits using KS tests.
problem Non-stationary multi-armed bandit problems.
method Active detection of change points using KS tests and adaptive Thompson Sampling.
result Sub-linear regret demonstrated for the two-armed bandit case.
The importance of adequately modeling credit risk has once again been highlighted in the recent financial crisis. Defaults tend to cluster around times of economic stress due to poor macro-economic conditions, {\em but also} by directly triggering each other through contagion. Although credit default swaps have radical…
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.
Paper optimizes UAV-assisted mobile edge computing for energy efficiency.
problem Minimizing energy consumption in UAV-assisted mobile edge computing.
method Proposes CAT and RAT algorithms combining convex optimization and deep reinforcement learning.
result RAT achieves similar performance and outperforms traditional algorithms.
Deep learning optimizes vehicular communication zones for efficient data dissemination.
problem Overdimensioning and inefficient communication in vehicular floating content.
method Deep learning is used to select optimal broadcasting areas (Anchor Zones) for efficient message dissemination.
result The proposed method achieves an accuracy of 89.7% in predicting optimal Anchor Zones, saving up to 27% of resources.
A smart device optimizes server selection for energy and latency in dynamic networks.
problem Optimizing server selection for mobile devices in edge computing with uncertainty and dynamic changes.
method Formulated as a budget-limited multi-armed bandit problem, a policy is proposed to minimize regret.
result The proposed method outperforms existing solutions in terms of energy and latency.
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.
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.
Unsupervised meta-learning speeds up reinforcement learning tasks.
problem Efficiently solving new reinforcement learning tasks.
method Formulating unsupervised meta-reinforcement learning and using mutual information for task proposals.
result Unsupervised meta-reinforcement learning effectively acquires accelerated procedures without manual task design.
This work characterizes model compression techniques for deep learning on embedded systems.
problem Efficient deep inference on resource-constrained devices.
method Extensive experiments on 11 neural network architectures using data quantization and pruning.
result Opportunities to achieve fast deep inference on embedded systems exist but require careful compression settings.
A parallel optimization method for convex functions using Hessian sketching and debiasing.
problem Massively parallel optimization of convex functions with limited communication.
method Newton method with Hessian sketching and debiasing by workers, server averages descent directions.
result Approximation of Newton step with low-complexity adaptive sketching scheme.
ARDEN improves deep learning performance on mobile devices by protecting privacy in the cloud.
problem Balancing privacy and performance in mobile deep learning with limited device capacity.
method ARDEN partitions DNN across mobile devices and cloud, using data transformation and noise addition for privacy, and noisy training for robustness.
result ARDEN enhances inference performance on cloud while maintaining strong privacy.
Federated learning optimizes task and resource allocation in balloon networks.
problem Minimizing energy and time consumption in task computation and transmission.
method SVM-based federated learning algorithm to dynamically adjust user associations, service sequences, and task allocations.
result Reduces the weighted sum of energy and time consumption by up to 16.1%.
Proposes D2D-LSTM for predicting mobile social network content diffusion paths.
problem Lack of accurate content popularity prediction considering time and location in mobile social networks.
method D2D-LSTM, a deep neural network combining user social features and files features.
result Significantly improved prediction accuracy (up to 85.858%) and faster convergence (less than 100 steps).
Origami uses SGX enclaves and blinding to protect deep neural network inference privacy.
problem Protecting deep neural network inference privacy in machine learning services.
method Combines enclave execution, cryptographic blinding, and accelerator-based computation.
result Demonstrates improved privacy-preserving inference performance compared to prior work.
Corella protects client data privacy in multi-server learning with correlated queries.
problem Protecting client data privacy in multi-server machine learning.
method Proposes a private multi-server learning approach using correlated queries and strong noise.
result Mitigates client data leakage with high accuracy and minimal computational effort.