Crowdsourcing is a promising human-in-the-loop paradigm for processing computer hard tasks by harnessing crowd intelligence. However, canonical crowdsourcing systems mostly need to aggregate/transmit worker data and may lead to privacy-leakage. To tackle this problem, we propose a novel approach, called FedCS (Federated CrowdSourcing), to achieve privacy protection while ensuring quality. FedCS aggregates model parameters from clients to build a shared server model while keeping the training data locally on worker devices to protect data privacy. To mitigate the staleness of stragglers and boost efficiency, we introduce a semi-asynchronous federated crowdsourcing mechanism, where the parameter server performs global aggregation periodically. Moreover, due to the different frequencies of workers participating in asynchronous update, FedCS uses a staleness-aware grouping and weighted aggregation heuristic to balance the training process. To speed up the convergence rate and improve the training accuracy, FedCS deploys adaptive learning step size for worker devices by their participation frequency. We further present a task assignment algorithm to help workers choose worthy and suitable tasks for annotations and to save the budget. Extensive experiments on benchmark datasets and a real-world crowdsourcing project show that FedCS can complete secure crowdsourcing projects with high quality and low budget.

Crowdsourcing is a promising computing paradigm for processing computer-hard tasks by harnessing human intelligence. How to protect online workers' privacy is a hindrance for deploying crowdsourcing in the real world. Attempts have been made to address this issue by injecting noise or encrypting sensitive data, which cause quality loss and/or heavy computation and communication load. In this paper, we propose an approach, called FedTA (Federated Worthy Task Assignment for Crowd Workers), to protect a crowd worker's private data while ensuring quality. FedTA trains a client model based on the private data and annotations owned by a worker and uploads client models to aggregate the server model, without leaking the privacy of task data. To account for the varying task distributions (i.e., non-i.i.d.) and error-prone annotations of tasks, it leverages the feature similarity and semantic similarity separately derived from client and server models on local tasks, to quantify the quality of annotations and clients. Based on those, it further introduces a task assignment strategy to notify the clients which tasks are worthy and suitable for annotations. This strategy can incrementally improve the performance of client and server models. At the same time, it disregards the unworthy tasks to save the budget and to avoid their negative impact. Experimental results show that FedTA can complete secure crowdsourcing projects with high quality and low budget.

Crowdsourcing is a favorable computing paradigm for processing computer-hard tasks by harnessing human intelligence. However, generic crowdsourcing systems may lead to privacy-leakage through the sharing of worker data. To tackle this problem, we propose a novel approach, called iFedCrowd (incentive-boosted Federated Crowdsourcing), to manage the privacy and quality of crowdsourcing projects. iFedCrowd allows participants to locally process sensitive data and only upload encrypted training models, and then aggregates the model parameters to build a shared server model to protect data privacy. To motivate workers to build a high-quality global model in an efficacy way, we introduce an incentive mechanism that encourages workers to constantly collect fresh data to train accurate client models and boosts the global model training. We model the incentive-based interaction between the crowdsourcing platform and participating workers as a Stackelberg game, in which each side maximizes its own profit. We derive the Nash Equilibrium of the game to find the optimal solutions for the two sides. Experimental results confirm that iFedCrowd can complete secure crowdsourcing projects with high quality and efficiency.

Crowdsourcing is a popular and relatively economic way to harness human intelligence to process computer-hard tasks. Due to diverse factors (i.e., task difficulty, worker capability, and incentives), the collected answers from various crowd workers are of different quality. Many approaches have been proposed to manage high quality answers and to reduce the budget by modelling tasks, workers, or both. However, most of the existing approaches implicitly assume that the capability of workers is fixed during the crowdsourcing process. But in practice, such capability can be improved by gradually completing easy to hard tasks, alike human beings’ intrinsic self-paced learning ability. In this paper, we investigate crowdsourcing with self-paced workers, whose capability can be gradually boosted as he/she scrutinises and completes easy to hard tasks. Our proposed SPCrowd (Self-Paced Crowd worker) first asks workers to complete a set of golden tasks with known annotations; provides feedback to assist workers with capturing the raw modes of tasks and to spark the self-paced learning, which in turn facilitates the estimation of workers’ quality and tasks’ difficulty. It then introduces a task difficulty model to quantify the difficulty of tasks and rank them from easy to hard, and a benefit maximization criterion for task assignment, which can dynamically monitor the quality of self-paced workers and assign the sorted tasks to capable workers. In this way, a worker can successfully complete hard tasks after he/she completes easier and related tasks. Experimental results on semi-simulated and real crowdsourcing projects show that SPCrowd can better control the quality and save the budget compared to competitive baselines.