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Found 7 papers, 2 papers with code
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e. g., mobile devices, IoT edge nodes).
SymED: Adaptive and Online Symbolic Representation of Data on the Edge
Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols.
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
In this paper, we present an energy-aware approach for the design and deployment of self-adaptive AI-based applications that can balance application objectives (e. g., accuracy in object detection and frames processing rate) with energy consumption.
Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions
This paper investigates the existing resource management approaches in Cloud Data Centres for energy and thermal efficiency.
Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning
Specifically, we propose a gradient boosting machine learning model for temperature prediction.
Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks
The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources.
Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries.
