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Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning
Federated Learning rests on the notion of training a global model distri...
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Reliable Federated Learning for Mobile Networks
Federated learning, as a promising machine learning approach, has emerge...
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FMore: An Incentive Scheme of Multi-dimensional Auction for Federated Learning in MEC
Promising federated learning coupled with Mobile Edge Computing (MEC) is...
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Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity
Originated from distributed learning, federated learning enables privacy...
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Toward an Automated Auction Framework for Wireless Federated Learning Services Market
In traditional machine learning, the central server first collects the d...
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A Federated Learning Approach for Mobile Packet Classification
In order to improve mobile data transparency, a number of network-based ...
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Fast-Convergent Federated Learning
Federated learning has emerged recently as a promising solution for dist...
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Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedly train and globally share models without revealing their local data. This technique can not only significantly improve privacy protection for mobile devices, but also ensure good performance of the trained results collectively. Currently, most the existing studies focus on optimizing federated learning algorithms to improve model training performance. However, incentive mechanisms to motivate the mobile devices to join model training have been largely overlooked. The mobile devices suffer from considerable overhead in terms of computation and communication during the federated model training process. Without well-designed incentive, self-interested mobile devices will be unwilling to join federated learning tasks, which hinders the adoption of federated learning. To bridge this gap, in this paper, we adopt the contract theory to design an effective incentive mechanism for simulating the mobile devices with high-quality (i.e., high-accuracy) data to participate in federated learning. Numerical results demonstrate that the proposed mechanism is efficient for federated learning with improved learning accuracy.
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