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08/31/2023
FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout
Federated Learning (FL) requires frequent exchange of model parameters, ...
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07/19/2023
A3D: Adaptive, Accurate, and Autonomous Navigation for Edge-Assisted Drones
Accurate navigation is of paramount importance to ensure flight safety a...
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07/04/2023
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Graph Neural Networks (GNNs) have gained growing interest in miscellaneo...
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04/22/2023
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 ar...
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02/01/2023
Task Placement and Resource Allocation for Edge Machine Learning: A GNN-based Multi-Agent Reinforcement Learning Paradigm
Machine learning (ML) tasks are one of the major workloads in today's ed...
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01/16/2023
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 collaborati...
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06/12/2020
Towards Flexible Device Participation in Federated Learning for Non-IID Data
Traditional federated learning algorithms impose strict requirements on ...
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03/12/2020
Machine Learning on Volatile Instances
Due to the massive size of the neural network models and training datase...
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11/21/2019
