Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping data locally can ensure data privacy, but also makes the server difficult to assess data quality, leading to the noisy data issue. Specifically, for any given training task, only a portion of each client's data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. The insight lies in mitigating the impact of noisy data by selecting appropriate clients and incentivizing them to clean their data spontaneously. Further, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods have been well-conducted to validate the effectiveness of the proposed mechanism.

We study an intriguing and practical scenario of online Spatial Crowdsourcing (SC), in which workers have the flexibility to perform tasks using various methods, such as walking, driving, or utilizing remote aerial vehicles (RAVs). This results in workers having heterogeneous, arbitrary, and non-stationary utilities over time. We refer to this scenario as integrated SC. Unfortunately, existing studies are limited in addressing integrated SC settings due to two aspects: (1) these studies are based on the assumption that workers’ utilities are independently and identically distributed and follow a stationary distribution like Gaussian, which does not hold in integrated SC; (2) their approaches fail to provide personalized privacy preservation for different workers. Motivated by these limitations, we closely investigate the heterogeneous utility and personalized privacy requirement in integrated SC and propose an Online Personalized Privacy-preserving Selection framework (OPPS). In this framework, we present an online selection policy that balances the exploration-exploitation trade-off given heterogeneous utilities and develop a built-in privacy policy that ensures differential privacy guarantee. We then demonstrate that our framework effectively addresses the trade-off by deriving a sublinear, privacy-related upper bound on regret that scales as$O(\sqrt{T})$. Extensive numerical simulations based on real-world drone datasets are conducted to validate the effectiveness of our framework compared with state-of-the-art approaches.