Federated learning is a distributed machine learning framework that enables distributed nodes with computation and storage capabilities to train a global model while keeping distributed-stored data locally. This process can promote the efficiency of modeling while preserving data privacy. Therefore, federated learning can be widely applied in distributed conjoint analysis scenarios, such as smart plant protection systems, in which widely networked IoT devices are used to monitor the critical data of plant production to improve crop production. However, the data collected by different IoT devices can be dependent and identically distributed (non-IID), causing the challenge of statistical heterogeneity. Studies have also shown that statistical heterogeneity can lead to a marked decline in the efficiency of federated learning, making it challenging to apply in practice. To promote the efficiency of federated learning in statistical heterogeneity scenarios, an adaptive client selection algorithm for federated learning in statistical heterogeneous scenarios called ACSFed is proposed in this paper. ACSFed can dynamically calculate the possibility of clients being selected to train the model for each communication round based on their local statistical heterogeneity and previous training performance instead of randomly selected clients, and clients with heavier statistical heterogeneity or bad training performance would be more likely selected to participate in the later training. This client selection strategy can enable the federated model to learn the global statistical knowledge faster and thereby promote the convergence of the federated model. Multiple experiments on public benchmark datasets demonstrate these improvements in the efficiency of the models in heterogeneous settings.

Mobile Crowd-Sensing (MCS) is a dominant sensing paradigm for Internet of Things (IoT), with a lot of potentials as it allows data collection through the user’s sensor embedded mobile devices. The participation of people in IoT not only brings greater flexibility and sensing ability but also increases the risk of privacy breaches to the participants. Primarily, a worker’s location data is vulnerable to information leaks as the task assignment in MCS is location-based. Most existing mechanisms that preserve worker’s location in MCS are designed under the assumption that the platform is trusted, which may be not valid in realworld applications. Besides, the existing studies focus either on task selection problem for workers or task assignment problem for the platform. Therefore, this paper investigates both task bidding and assignment while preserving location privacy. We propose two task selection strategies: Minimize Total Cost (MTC) and Minimize Average Cost (MAC). Each worker submits a cost that is obfuscated using differential privacy to the platform. We propose probability cost-efficient worker selection mechanism (PCE-WSM) to determine winners and probability individual-rationality critical payment mechanism (PIR-CPM) to determine payments to winners. We prove that PIR-CPM is truthful and can achieve probability-individual rationality by theoretical analysis. To evaluate our proposed strategies, we conduct extensive experiments on both synthetic and real-world datasets, and the experimental results validate that PCE-WSM can achieve enough privacy preservation without incurring a high payment.