A Learning-Based Incentive Mechanism for Federated Learning

By Yufeng Zhan, Peng Li, Zhihao Qu, Deze Zeng, Song Guo. IEEE Internet of Things Journal, 2020

534 citations recorded by the source corpus.

Internet of Things (IoT) generates large amounts of data at the network edge. Machine learning models are often built on these data, to enable the detection, classification, and prediction of the future events. Due to network bandwidth, storage, and especially privacy concerns, it is often impossible to send all the IoT data to the data center for centralized model training. To address these issues, federated learning has been proposed to let nodes use the local data to train models, which are then aggregated to synthesize a global model. Most of the existing work has focused on designing learning algorithms with provable convergence time, but other issues, such as incentive mechanism, are unexplored. Although incentive mechanisms have been extensively studied in network and computation resource allocation, yet they cannot be applied to federated learning directly due to the unique challenges of information unsharing and difficulties of contribution evaluation. In this article, we study the incentive mechanism for federated learning to motivate edge nodes to contribute model training. Specifically, a deep reinforcement learning-based (DRL) incentive mechanism has been designed to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes. Finally, numerical experiments have been implemented to evaluate the efficiency of the proposed DRL-based incentive mechanism.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/08a9c7ba8868ad41bcf911ea389c8dc184922627

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Citations in the corpus

Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “This is due to evaluation methods that can directly measure the performance of their contributed model, evaluating a machine learning model is easier and more concrete than evaluating the contribution of raw sensing data [158].”
Federated Learning for Open Banking (2021, background): “Diﬀerent forms of incentives are possible, such as user-deﬁned utility and money-based rewards, and have been well discussed in the literature [20,21,36,39].”
Decentralized Federated Learning: A Survey and Perspective (2023, background): “…values indicating the relative strength of the respective FL approach in that domain. metrics, which are the focal points of current state-of-the-art (SOTA) research and worthy of further investigation [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48].”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, background): “Thus, developing solutions to address security and privacy issues is highly needed before FLchain can be deployed at a large scale.”
Federated learning for 6G communications: Challenges, methods, and future directions (2020, background): “…how to design data quality-based incentive mechanisms to motivate more devices with high-quality data to participate in federated learning and obtain higher rewards for their high-quality data contributions, thus improving both the system reliability and the learning performance [7], [39], [41].”
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, unknown): “In [196], [203], [204], where mobile devices are connected to the edge server, the authors design the incentive mechanism for efficient edge learning.”
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2020, methodology): “As an extension, the study in [44] uses a Stackelberg game formulation together with Deep Reinforcement Learning to design a learning-based incentive mechanism for FL.”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, methodology): “Unlike the aforementioned approaches to solving Stackelberg formulations, a DRL-based approach is adopted together with the Stackelberg game as proposed in [138].”
Federated Learning: Opportunities and Challenges (2021, background): “To encourage/improve device participation in FL, works such as [15, 16, 37, 39] propose a reputation based incentive mechanism i.”
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, background): “[53] design a deep reinforcement learning (DRL) based incentive”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, background): “In [178], the authors design an incentive mechanism to motivate edge nodes to participate in the federated training.”
Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning (2020, unknown): “As a result, DNNs are being increasingly adopted for the design of learning-based incentive mechanisms [13].”