Towards Collaborative Edge Intelligence: Blockchain-Based Data Valuation and Scheduling for Improved Quality of Service +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). [PERSON]  
1 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; [EMAIL_ADDRESS] (Y.D. ); [EMAIL_ADDRESS] (Z.W. );  
[EMAIL_ADDRESS] (C.L.) [PERSON]  
1 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; [EMAIL_ADDRESS] (Y.D. ); [EMAIL_ADDRESS] (Z.W. );  
[EMAIL_ADDRESS] (C.L.) [PERSON]  
1 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; [EMAIL_ADDRESS] (Y.D. ); [EMAIL_ADDRESS] (Z.W. );  
[EMAIL_ADDRESS] (C.L.) [PERSON]  
1 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada; [EMAIL_ADDRESS] (Y.D. ); [EMAIL_ADDRESS] (Z.W. );  
[EMAIL_ADDRESS] (C.L.) Footnote 1: email: {[EMAIL_ADDRESS]}  
+++ ==WARNING: Truncated because of repetitions==
.ca}  
++  
a fundamental question: Apart from model design, can the QoS of edge intelligence be improved by device collaboration and data conditioning at the network edge? In this work, we focus on the exploration of an affirmative answer to the above question. We aim to give a simple yet effective framework to motivate collaboration for improved QoS at the network edge. Fortunately, distributed machine learning (DML) provides an opportunity to realize collaborative edge intelligence. DML is a paradigm that enables the training of ML models across multiple decentralized edge devices without exchanging private local data samples directly. Federated learning (FL) [4] is a well-known example of collaborative DML. In the context of FL, private and distributed data are not allowed to be shared for privacy reasons, as the model is trained locally by mini-batch stochastic gradient descent (SGD) on each participant's private data, and only model weights are communicated to a central server for global model aggregation. FL is especially beneficial for AI to learn private information under data regulations [5]. For instance, smart health applications rely on IoT data: especially private health data [4]. Instead of uploading IoT data, users transmit model weights trained from health data for prediction. Despite its privacy preservation feature, FL suffers from free-riding attacks [6; 7; 8], wherein malicious participants exploit the system by benefiting from the global model without meaningful contributions, thereby compromising the efficiency and fairness of the collaborative learning process. Moreover, FL requires a centralized parameter server, which may constitute a single point of failure (SPOF). Therefore, a decentralized approach is needed to enhance system security and reliability. As a distributed ledger technology, blockchain is a decentralization approach with consecutive timestamps to solve the above issues. Additionally, data-centric methods have recently emerged for understanding and conditioning data in the context of centralized ML [9; 10; 11]. Compared with large datasets containing biased information, a small but unbiased dataset tends to have more useful information [9]. Therefore, data valuation and scheduling are needed before feeding data into an ML model such as a deep neural network (DNN). Intuitively, the more useful information that a DNN can obtain from data, the more accurate prediction results we can achieve. However, IoT data are often heterogeneous at the network edge. User data depend on living habits, preferences and locations. They are often not independent and identically distributed (non-IID).