Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning
By Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusist Niyato, Cyril Leung, Chunyan Miao, Qiang Yang. 2020
26 citations recorded by the source corpus.
In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous. This necessitates a paradigm shift from the current cloud-centric model training approach to the Edge Computing based collaborative learning scheme known as edge learning, in which model training is executed at the edge of the network. In this article, we first introduce the principles and technologies of collaborative edge learning. Then, we establish that a successful, scalable implementation of edge learning requires the communication, caching, computation, and learning resources (3C-L) of end devices and edge servers to be leveraged jointly in an efficient manner. However, users may not consent to contribute their resources without receiving adequate compensation. In consideration of the heterogeneity of edge nodes, e.g., in terms of available computation resources, we discuss the challenges of incentive mechanism design to facilitate resource sharing for edge learning. Furthermore, we present a case study involving optimal auction design using Deep Learning to price fresh data contributed for edge learning. The performance evaluation shows the revenue maximizing properties of our proposed auction over the benchmark schemes.
Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Praneeth Vepakomma
- Ramesh Raskar
- Abhishek Singh
- Otkrist Gupta
- Chaoyang He
- Quoc V. Le
- Murali Annavaram
- Martin Wistuba
In Computer Networks and Communications
In Computer Science Applications
Citations in the corpus
- NoPeek: Information leakage reduction to share activations in distributed deep learning (2020, methodology): “Split learning has been adopted in Internet of Things (IOT) and edgedevice machine learning settings in [20, 21, 22, 23] including multi-modal fusion based machine learning across edge devices in [24, 25] with data collected at the edge on imagery and millimeter wave (mmWave) radio frequency (RF) si”