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Found 9 papers, 1 papers with code
Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach
Therefore, it is crucial to motivate edge devices to become learners and offer their computing resources, and either offer their private data or receive the needed data from the orchestrator and participate in the training process of a learning task.
Energy-Efficient Multi-Orchestrator Mobile Edge Learning
The heterogeneity in edge devices' capabilities will require the joint optimization of the learners-orchestrator association and task allocation.
Task Allocation for Asynchronous Mobile Edge Learning with Delay and Energy Constraints
The proposed HA asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) equal batch allocation scheme.
Jointly Optimizing Dataset Size and Local Updates in Heterogeneous Mobile Edge Learning
This paper proposes to maximize the accuracy of a distributed machine learning (ML) model trained on learners connected via the resource-constrained wireless edge.
Adaptive Task Allocation for Asynchronous Federated and Parallelized Mobile Edge Learning
This paper proposes a scheme to efficiently execute distributed learning tasks in an asynchronous manner while minimizing the gradient staleness on wireless edge nodes with heterogeneous computing and communication capacities.
Adaptive Task Allocation for Mobile Edge Learning
This paper aims to establish a new optimization paradigm for implementing realistic distributed learning algorithms, with performance guarantees, on wireless edge nodes with heterogeneous computing and communication capacities.
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced.
VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning Approach
In this paper, we design a multimodal framework for object detection, recognition and mapping based on the fusion of stereo camera frames, point cloud Velodyne Lidar scans, and Vehicle-to-Vehicle (V2V) Basic Safety Messages (BSMs) exchanged using Dedicated Short Range Communication (DSRC).
Joint Indoor Localization and Radio Map Construction with Limited Deployment Load
In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment.
