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Found 9 papers, 0 papers with code
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data.
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints.
A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones
Accurate navigation is of paramount importance to ensure flight safety and efficiency for autonomous drones.
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures.
Towards Carbon-Neutral Edge Computing: Greening Edge AI by Harnessing Spot and Future Carbon Markets
Provisioning dynamic machine learning (ML) inference as a service for artificial intelligence (AI) applications of edge devices faces many challenges, including the trade-off among accuracy loss, carbon emission, and unknown future costs.
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally.
Towards Flexible Device Participation in Federated Learning
Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning.
Machine Learning on Volatile Instances
Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes.
Observe Before Play: Multi-armed Bandit with Pre-observations
We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round.
