# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 2 Related Work  
Federated learning [8] was first proposed by Google in 2016, based on the new paradigm of data immobilization but model moving, which effectively guarantees data privacy of edge data owners. For distributed computing, different data distributions will greatly affect model aggregation. [PERSON] et al. [9] subsequently proposed a FedAvg algorithm with better robustness to improve the model aggregation effect by randomly selecting and averaging the aggregation method for non-Independent Identically Distribution (non-IID) data in federated learning. In addition, [PERSON] et al. [10] proposed a method to share a small portion of training data through global sharing and the definition of weight scatter, which refers to the distribution deviation of local model weights towards the local data due to the differences in the distribution of training data. To counteract this weight deviation, [PERSON] et al. [11] proposed a reinforcement learning-based control framework FAVOR, which is realized by intelligently selecting data owner sequences in each communication round, with the benefit of accelerating model convergence. Similar to the barrel effect, the convergence speed of global model in federated learning is greatly determined by the data owner with the slowest training time. Improper data owner selection will greatly increase the global aggregation time. [PERSON] et al. [12] proposed a data owner selection strategy based on dividing the training time hierarchy to cope with the'slow' participants. To maximize the convergence speed of global model, [PERSON] et al. [7] ensured holding a saturated amount of training data while minimizing the total energy consumption of the participant. Similarly, [PERSON] et al. [13] proposed a Data Distribution Aware Online data owner Selection (DOCS) algorithm to minimize the convergence time while ensuring high learning accuracy. Different from the above work, considering the increasingly powerful mobile processors, the main factor influencing the data owner's training speed is the CPU frequency. In practice, the frequency of model training fluctuates so that it cannot reach the rated value. Our work proposes a training time prediction module to solve the above problem. To cope with possible backdoor attacks, gradient attacks, and model poisoning attacks in federated learning, [PERSON] et al. [14] first systematically investigated the impact of localized model poisoning attacks on federated learning. Then, [PERSON] et al. [15] proposed the FLDetector method to detect malicious data owners by comparing the consistency of data owners' model updates over multiple iterations through the observed differences in model updates of the attacked model. To prevent attacks, [PERSON] et al. [16] proposed MUD-HoG, a malicious data owner attack defense algorithm based on the long-short history gradient, which can differentially treat detected malicious and low-reputation data owners and identify whether there was a targeted attack from malicious data owners. [PERSON] et al. [17] proposed a lightweight protocol that allows participants to privately exchange and mix their updated encrypted fragments, achieving client-side privacy protection, and designed a reputation-based defense measure to achieve security. Proper incentives can motivate data owners to actively participate in model training in a federated system. [PERSON] et al. [4] improved the incentive effect for highly reputable mobile devices with high-quality data by effectively combining reputation and contract theory, whereas [PERSON] et al. [18] introduced the Stackelberg game to participate in the incentives. [PERSON] et al. [19] measured the marginal contribution based on the data owner's local model. However, the existing scenarios cannot satisfy the case where the data owner's cost is unknown, and, once the payment is lower than the expected, the data owner's motivation will be greatly undermined. Based on this, [PERSON] et al.