However, DML relies on a training method named mini-batch SGD, which is designed under the assumption that data are IID [4; 5]. A mismatch between model and real data decreases a model's prediction accuracy. In this paper, we consider two factors that violate the IID property of data: label distribution skew (i.e., users produce biased data) and data quantity skew (i.e., users produce different amounts of data). These factors are particularly significant in DML scenarios where, due to beneficial data protection regulations, raw data are not directly accessible during model training. Without knowledge of data valuation, it is difficult to apply data-centric methods at a network edge. To solve the above challenges, we propose a blockchain-based incentive mechanism to diversify rewards received by different contributors according to decentralized data valuation. Intuitively, an up-to-date model is of greater utility. Therefore, we aim to ensure every participant will leave the system with a global model reflecting its incremental contributions in the form of data valuation. Data valuation can be recorded on the blockchain, which is maintained by a group of entities instead of a centralized server. Therefore, data valuation becomes a consensus among all entities. By replacing one-server decisions with a group consensus, the evaluation of data valuation is decentralized. However, the consensus mechanism in blockchain, e.g., proof-of-work (PoW), is not suitable for data valuation recording because it requires extensive computing resources, which delays the training process of DML [12]. In this paper, we investigate a blockchain based on multi-access edge computing (MEC) servers, where a delegated proof-of-stake (DPoS) [13] is adopted as the blockchain consensus mechanism. We refer to the MEC server simply as a server because there is only one kind of server in this paper. As a server is located closer to users than a cloud computing center, communication latency in DML is further reduced [12]. Instead of uploading local data distributions [14], valuations of decentralized data are based on private validation datasets. As private information is not included in blockchain records, data valuation is evaluated privately. To the best of our knowledge, this is the first work that explores blockchain's potential in diversifying model rewards by decentralized data valuation and data scheduling. Our major contributions are summarized as follows:  
* A time-dependent incentive mechanism is proposed atop blockchain to diversify model rewards. It improves the QoS of collaborative edge intelligence while preventing free-riders from using the system. * We propose a decentralized data valuation method combining cross-validation and DPoS consensus to mitigate information loss of DML on heterogeneous data. The valuation function achieves group rationality, fairness and additivity at the network edge. * To maximize the marginal utility of data samples, a curriculum data scheduling approach is designed. With an adaptive moving window, the efficiency of data scheduling is improved with reduced latency. The rest of this paper is organized as follows. Related works are summarized in Section 2. The system model and problem formulation are described in Section 3. Our proposed blockchain-based incentive mechanism is given in Section 4 to defend against free-rider attacks. An adaptive curriculum learning method is further proposed in Section 5 to improve the QoS of each user. We further propose our algorithms in Section 6 as an optimized solution to the formulated QoS problem. Performance evaluation results on non-IID datasets are provided in Section 7. Finally, we conclude this paper in Section 8.