## 8 Conclusions  
In this paper, a time-dependent and decentralized data valuation approach has been proposed to improve the QoS of collaborative edge intelligence and defend against free-riding attacks. By considering information loss and diminishing marginal utility of data, a robust aggregation algorithm has been proposed for improving DML on non-IID data. Based on experimental results, we have shown that the QoS of edge intelligence can be improved by decentralized device collaboration and curriculum data scheduling at the network edge. We have improved DML by evaluating and understanding data rather than designing finely tuned models. Our blockchain-enabled data-centric method has been shown to be simple yet effective at improving the fairness and performance of DML. Based  
Figure 11: Distinct data valuation helps discriminate attackers: decentralized valuation of data contribution shows superior discriminative performance.  
Figure 10: Superior resilience against free-riding attacks: free-riders that conduct random weights and additive noise attacks do not harm the QoS of collaborative edge intelligence.  
on experiments and observations, we conclude that decentralization of data valuation with scheduling is a promising approach towards collaborative edge intelligence.  
Despite the advantages of our approach, we remind the reader that maintaining local validation data on every smartphone is still required for cross-validation. For future work, we will study a data-free valuation framework. Additionally, the privacy issue of uploading model weights is not the focus of this paper. Therefore, we will also study how to improve the differential privacy of honest contributors in a blockchain network.  
Conceptualization and methodology, Y.D., Z.W. and C.L.; resources, V.C.M.L.; data curation, Y.D.; writing--original draft preparation, Y.D.; writing--review and editing, C.L., Z.W. and V.C.M.L.; visualization, Y.D.; supervision, C.L., Z.W. and V.C.M.L.; project administration, Z.W.; funding acquisition, V.C.M.L., Z.W. and C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Blockchain@UBC, by the Natural Sciences and Engineering Research Council (NSERC) of Canada (CREATE grant 528125 and grants RGPIN-2019-06348, RGPIN-2020-05410, RGPIN-2021-02970 and DGECR-2021-00187), by Public Safety Canada (NS-5001-22170), by the Guangdong Pearl River Talent Recruitment Program (grant 2019 ZT08X603), by Guangdong Pearl River Talent (grant 2019 JC01X235), by the Shenzhen Science and Technology Innovation Commission (grant R2020A045) and by the UBC PMC-Sierra Professorship in Networking and Communications.  
The datasets used in this study are derived from public domain resources. The CIFAR-10 and CIFAR-100 datasets are available from the Canadian Institute for Advanced Research (CIFAR) at: [[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)) accessed on 30 June 2024. The Human Activity Recognition (HAR) dataset is available from the UCI Machine Learning Repository at: [[https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones)]([https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones)) accessed on 30 June 2024. These datasets are widely used benchmarks in the machine learning community and are freely accessible for research purposes.  
Simulations were enabled by support from the Cedar Cluster at the Digital Research Alliance of Canada.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.