Market Your Venue with Mobile Applications: Collaboration of Online and Offline Businesses

Yu, Haoran; Iosifidis, George; Shou, Biying; Huang, Jianwei

doi:10.1109/infocom.2018.8486019

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…Stackelberg game is a sequential model of game theory commonly used to formulate the interactions between different players in the sell or procurement of common products, making it more appropriate for the incentive design in FL [16], [17], [35]. In Stackelberg game, there exist two categories of players, i.e., leader and follower.…”

Section: B Stackelberg Gamementioning

confidence: 99%

“…Most of studies consider the FL ecosystem as a monopoly market, where the single leader is the model owner or the cloud server and the followers are multiple participants performing Cournot game in the second stage. These studies are conducted in scenarios of mobile edge computing [29], distributed coded machine learning [35], IoTs [51], etc. On the contrary, Feng argues that model owner uses the learning service provided by mobile devices, and the model owner acts as a single follower in the lower level of Stackelberg game with multiple leaders [18].…”

Section: B Stackelberg Gamementioning

confidence: 99%

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A Comprehensive Survey of Incentive Mechanism for Federated Learning

Zeng,

Wang

et al. 2021

Preprint

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Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning. In such promising paradigm, the performance will be deteriorated without sufficient training data and other resources in the learning process. Thus, it is quite crucial to inspire more participants to contribute their valuable resources with some payments for federated learning. In this paper, we present a comprehensive survey of incentive schemes for federate learning. Specifically, we identify the incentive problem in federated learning and then provide a taxonomy for various schemes. Subsequently, we summarize the existing incentive mechanisms in terms of the main techniques, such as Stackelberg game, auction, contract theory, Shapley value, reinforcement learning, blockchain. By reviewing and comparing some impressive results, we figure out three directions for the future study.

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Section: B Stackelberg Gamementioning

confidence: 99%

Section: B Stackelberg Gamementioning

confidence: 99%

A Comprehensive Survey of Incentive Mechanism for Federated Learning

Zeng,

Wang

et al. 2021

Preprint

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“…Because its nature, the combination with the federated learning is widely researched to better predicted a value according to the accuracy of the model and to other strategies [88]. Also, for the high calculation power needed by Shapley Value in FL, many approximation algorithm are also proposed to accelerate the incentive evaluation progress [39] [98]. Other methods using the Game theory like Stackelberg Game [76], [45] problems or using reinforcement learning [89], [101] to automatically decided which client deserves better profit also have a good performance on this problem.…”

Section: B Incentive Mechanismmentioning

confidence: 99%

Towards Reliable Utilization of AIGC: Blockchain-Empowered Ownership Verification Mechanism

Chen,

Li,

et al. 2023

IEEE Open J. Comput. Soc.

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With the development of the blockchain technology, a decentralized and de-trusted network paradigm has been constructed, enabling multiple digital assets like NFT, to be permanently recorded and authenticated by blockchain. Also, the uniqueness and verifiability of these assets allows them to flow and generate value between any network entities. With the emergence of AI Generative Content (AIGC), the ownership of models and generative contents, which are also digital assets, has not been well protected. Both because the black-box nature of neural networks makes it difficult to mark models' ownership and because the lack of a reliable third party verification platform. Meanwhile, the existing model-attack threat and raising ethical problems driven the research on model watermark embedding for traceability and verification, and thus the reliable basic algorithm and the verification platform are needed. In this survey, while emphasizing the importance and reason of the ownership protection in AIGC and summarizing the recent research using model watermarking, we will also introduce the achievements of blockchain in copyright in order to summarize the research history and point out future direction of model copyright validation from both the underlying technology and the supporting platform.

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“…Within apps of same type such as games, some Q. Qureshi, N. Azila Bt Mohd Noor, Shahizan Bin Hassan, M. Imran Qureish 344 WWW.GJOES.ORG apps may be capable of handling ad type of certain types more efficiently. For example, Pokemon go was able to handle native ads in a very efficient manner whereas most game apps are capable of presenting only traditional advertisements (Yu, Iosifidis, Shou, & Huang, 2018). App type may moderate the relationship between incentives a consumer expects from in-app ads (X.…”

Section: Moderators Relevant To In-app Advertisementmentioning

confidence: 99%

Moderating Factors Affecting Intention to Access In-App Advertisements

Qureshi¹,

Noor²,

Hassan³

et al. 2019

GJOES

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Role of moderator in research is significant. Many researchers consider moderator in terms of control factors or constants; however, moderator can significantly increase value of research model. In this article, a review is made of recent literature to identify potential moderators for in-app advertisements adoption models. In-app advertisements is an evolving technology which is spreading at varying speed across the globe. There is scope of research in this field. Recent research articles are reviewed to identify potential moderators. Their proposed relationships in various scenarios are presented. At the end of the review, relevant moderators for in-app advertisement adoption models are presented with examples how researchers can find relevant moderators in a scenario.

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Market Your Venue with Mobile Applications: Collaboration of Online and Offline Businesses

Cited by 12 publications

References 16 publications

A Comprehensive Survey of Incentive Mechanism for Federated Learning

A Comprehensive Survey of Incentive Mechanism for Federated Learning

Towards Reliable Utilization of AIGC: Blockchain-Empowered Ownership Verification Mechanism

Moderating Factors Affecting Intention to Access In-App Advertisements

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