# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 1 Introduction  
Federated learning (FL) has become a promising data circulation technique to break data isolation and further promote secure data sharing, in consideration of raw data stored in each data owner's local device rather than a centralized data server [1; 2; 3; 4]. However, data owners' computing and communication resources have to be compromised during the FL training process, and thus they usually have no incentive to participate in the training process unless sufficient monetary compensation in return. Insufficient data owners' training data hinders the aggregated model accuracy, thus failing the whole FL training. Thus, how to incentivize abundant data owners' participation is a key problem in FL. Most prior works [1; 3] adopted a multi-dimensional reverse auction to incentivize participants and facilitate FL, where the central server is the buyer, and data owners are reversely considered as the sellers to trade the use of multiple resources by a bid to reflect the minimum acceptable payment once chosen as a winner. However, a recent work [5] selected data owners only by their claimed data quantity or privacy budget, but neglected some key factors like data quality and device training time. Although other few works [1; 2; 6] also measured data quality, they designed quality detection according to data distribution similarity or aggregated reputations and thus cannot be applied to our scenario directly. More importantly, they failed to choose data owners to achieve fast federated learning. To facilitate model training, [PERSON] et al. [7] also considered model convergency speed, and they failed to achieve a satisfying training time guarantee. To address the above limitations simultaneously, it is nontrivial to design an efficient incentive mechanism to select cost-effective data owners with high computing capability under a limited budget. However, three key challenges have to be addressed. The primary challenge is to quantify each data owner's contribution before the current FL training round. [PERSON] et al. [1] measured data owners' quality according to their claimed data amount, privacy budget, and data distribution. [PERSON] et al. [2] aggregated weighted reputation scores from multiple data requesters who ever selected them as a winner as these data owner's quality indicator. When the expected data distribution is unknown, or there are still no other data requesters who ever recruited him, their solution cannot work in our case. The next challenge is how to predict model training time. The CPU frequency cannot usually reach the data owner's claimed value due to the fluctuation in an actual working environment. The actual low CPU frequency degrades the convergence time of the global model. Thus, it is essential to predict each data owner's actual CPU frequency used for FL training. The last but not least challenge is how to prevent data owners from misreporting their multiple-dimensional bids for higher profit. A strategic data owner probably submits a false bid to manipulate the market, thus leading to the failure of the FL process. In response to the above challenges, we propose a quality-aware incentive mechanism, named QuoTa, for fast federated learning. It motivates data owners with high data quality, high computing capability, and low cost to participate in FL training. In particular, we first design a quality detection module to choose data owners with both large contribution and marginal contribution for the global model. Then, QuoTa exploited a training time prediction module to estimate each data owner's computing capability at the current round, in order to achieve faster convergence. Combined with these two modules, QuoTa propose an efficient data owner selection and payment strategy to achieve all desired economic properties. We highlight main contributions as follows:  
* We design a quality-aware incentive mechanism to achieve fast-federated learning. To the best of our knowledge, QuoTa is the first work to take data quality and model training time prediction into consideration.