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In the evolving landscape of Web 3.0, 5G/6G, and real-world applications, federated learning faces unique challenges. Traditional incentive mechanisms struggle to address the need to motivate both data owners to provide high-quality data and ...

• RFLF, A novel framework incentivises both data & model owners in federated learning.
• Dynamic, multi-stage incentives for equitable collaboration in federated learning.
• Empirically validated to improve cooperation and performance on ...

With the widespread popularity of smart devices in recent years, mobile crowdsensing (MCS) as a new appealing data collection paradigm has gained attention in urban monitoring, traffic prediction and social networks, which deploys on decentral ...

• IE decisions regarding new service product design in an age of sharing economy.
• Formulation of co-design problem between the service company and the digital platform operators.
• Modeling of co-design problem in accordance with game-...

This paper proposes a novel approach to address the evolving landscape of service product design, particularly in collaboration with digital platform operators. By formulating a nonlinear hierarchical interactive optimization model (HIO) based on ...

Hierarchical federated learning (HFL) can effectively alleviate the communication bottleneck of traditional federated learning. However, the long-term healthy development of federated learning needs to continue to attract reliable participants ...

• Design a reliable Hierarchical federated learning incentive mechanism.
• Propose a two-way reputation mechanism for trustworthy node selection.
• Define a data quality metric for clients and design contracts based on it.
• ...

Federated learning (FL) is vulnerable to Byzantine attacks due to its distributed nature. Existing defenses, which typically rely on server-based or trust-bootstrapped aggregation rules, often struggle to mitigate the impact when a large ...

• Combine incentives and ensemble learning to reward honesty, reduce malicious impact.
• BEIM ensures individual rationality, truthfulness, and budget feasibility.
• BEIM outperforms baselines in defending against malicious clients in ...

In the face of issues such as privacy leakage and malicious attacks, blockchain-based asynchronous federated learning emerges as a promising solution, not only capable of protecting user privacy and resisting malicious attacks but also ...

Crowdsourcing refers to the practice of outsourcing tasks previously performed by internal employees of an enterprise or organization to the general public through the internet in a free and voluntary manner, resulting in a mutually beneficial ...

Federated learning, a privacy-preserving collaborative machine learning paradigm, has led to the proposal of various incentive mechanisms to encourage active participation of data owners. However, most of the existing mechanisms focused on the ...

Within the intricate dynamics of a blockchain system, participants with bounded rationality may engage in dishonest behavior, thereby disrupting the consensus of the system in the absence of incentive mechanisms. This vulnerability is particularly ...

Federated Learning (FL) has gained significant traction as a promising approach to enable collaborative machine learning (ML) while safeguarding data privacy across diverse applications, with the Vehicle-to-Everything (V2X) environment being a ...

• Introduced a blockchain-based VFL framework for V2X, improving collaboration and privacy.
• Utilized efficient Bulletproofs technology with an incentive mechanism.
• Demonstrated security against data poisoning and efficient resource ...

Cooperative edge computing serves as an effective solution to provide reliable and elastic edge computing services through pooling geographically proximate edge resources and efficiently allocating them to users. The incentive mechanism is ...

Transaction efficiency is critical to the success of e-commerce platforms. We propose a Reverse Double Auction Mechanism (RDouMech), an innovative auction-based algorithm, to revolutionize the transaction process in e-commerce operations. It ...

• We propose RDouMech algorithm, which can achieve precise matching and effective pricing strategies, thereby increasing the transaction efficiency of e-commerce platforms.
• RDouMech allows multi-bid and multi-constraints, increases ...

Blockchain-based healthcare IoT technology research enhances security for smart healthcare services such as real-time monitoring and remote disease diagnosis. To incentivize positive behavior among participants within a blockchain-based smart ...

• A credible and practical scheme (BtRaI) is used to enhance the quality and efficiency of healthcare services.
• We propose a comprehensive and credible reputation assessment method to suppress malicious scoring attacks.
• We propose a ...

• Improved indiscriminate incentive mechanisms based on the different interests of multiple stakeholders.
• Three differential game models were constructed for digital ecosystem with different levels of cooperation intensity.
• Analyzed ...

The digital ecosystem has hastened the fusion of manufacturing and digital technology in realizing product and service value. However, due to the coordination dilemmas within the digital ecosystem such as risk sharing, competition and cooperation ...

With the development of mobile Internet and the popularity of public online consumption, the scale expansion of the e-commerce industry has driven the continuous growth of logistics business, but the increase in order volume has brought pressure ...

In the process of model training of the federated learning system, how to design an incentive mechanism to attract more high-quality worker nodes to join is a key issue. The existing researches on federated learning incentive mechanism only ...

The data privacy concern for mobile users (MUs) in mobile crowdsensing (MCS) has attracted significant attention. Federated Learning (FL) breaks down data silos, enabling MUs to train locally without revealing actual information. However, FL ...

Nowadays, the integration of blockchain technology with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has drawn the researcher attention because it can provide key security auditing and transaction traceability in the context of data ...

Federated Learning (FL) is an emerging field of research that contributes to collaboratively training machine learning models by leveraging idle computing resources and sensitive data scattered among massive IoT devices in a privacy-preserving ...

• Establish a energy-aware reputation management scheme.
• Conduct a client activation strategy to select high quality clients.
• Create a training decision strategy to make the optimal training epochs.
• Conduct simulations close to ...

Many Multi-Armed Bandit (MAB) based workers selection schemes have been proposed to select high-quality workers to enhance the quality of tasks. However, in Mobile Crowd Sensing (MCS), a complex mutual effect exists among task requestors, the MCS ...