Practical Concerns in Enforcing Ethereum Smart Contracts as a Rewarding Platform in Decentralized Learning


KIPS Transactions on Computer and Communication Systems, Vol. 9, No. 12, pp. 321-332, Dec. 2020
https://doi.org/10.3745/KTCCS.2020.9.12.321,   PDF Download:
Keywords: Blockchain, data privacy, Decentralized Learning, incentive mechanism, Smart Contract
Abstract

Decentralized approaches are extensively researched by academia and industry in order to cover up the flaws of existing systems in terms of data privacy. Blockchain and decentralized learning are prominent representatives of a deconcentrated approach. Blockchain is secure by design since the data record is irrevocable, tamper-resistant, consensus-based decision making, and inexpensive of overall transactions. On the other hand, decentralized learning empowers a number of devices collectively in improving a deep learning model without exposing the dataset publicly. To motivate participants to use their resources in building models, a decent and proportional incentive system is a necessity. A centralized incentive mechanism is likely inconvenient to be adopted in decentralized learning since it relies on the middleman that still suffers from bottleneck issues. Therefore, we design an incentive model for decentralized learning applications by leveraging the Ethereum smart contract. The simulation results satisfy the design goals. We also outline the concerns in implementing the presented scheme for sensitive data regarding privacy and data leakage.


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Cite this article
[IEEE Style]
S. Rahmadika, M. Firdaus, S. Jang and K. Rhee, "Practical Concerns in Enforcing Ethereum Smart Contracts as a Rewarding Platform in Decentralized Learning," KIPS Transactions on Computer and Communication Systems, vol. 9, no. 12, pp. 321-332, 2020. DOI: https://doi.org/10.3745/KTCCS.2020.9.12.321.

[ACM Style]
Sandi Rahmadika, Muhammad Firdaus, Seolah Jang, and Kyung-Hyune Rhee. 2020. Practical Concerns in Enforcing Ethereum Smart Contracts as a Rewarding Platform in Decentralized Learning. KIPS Transactions on Computer and Communication Systems, 9, 12, (2020), 321-332. DOI: https://doi.org/10.3745/KTCCS.2020.9.12.321.