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Found 13 papers, 4 papers with code
CGS-Mask: Making Time Series Predictions Intuitive for All
Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time.
Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks
In this paper, we propose Hierarchical Federated Learning with Momentum Acceleration (HierMo), a three-tier worker-edge-cloud federated learning algorithm that applies momentum for training acceleration.
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing
COVID-19 has spread rapidly across the globe and become a deadly pandemic.
Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply
With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery.
DONE: Distributed Approximate Newton-type Method for Federated Edge Learning
In this work, we propose DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning.
Stochastic Client Selection for Federated Learning with Volatile Clients
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public.
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.
Federated Learning with Nesterov Accelerated Gradient
It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far.
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts.
Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning
Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads.
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.
Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services.
Improving Raw Image Storage Efficiency by Exploiting Similarity
To improve the temporal and spatial storage efficiency, researchers have intensively studied various techniques, including compression and deduplication.
