FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training
Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is challenging to claim ownership rights and commercializ...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10012365/ |
| _version_ | 1797902249016426496 |
|---|---|
| author | Umer Majeed Latif U. Khan Sheikh Salman Hassan Zhu Han Choong Seon Hong |
| author_facet | Umer Majeed Latif U. Khan Sheikh Salman Hassan Zhu Han Choong Seon Hong |
| author_sort | Umer Majeed |
| collection | DOAJ |
| description | Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is challenging to claim ownership rights and commercialize the global model efficiently and transparently. Additionally, incentives need to be provided to ensure that devices participate in the FL process. In this paper, we propose a smart contract-based framework called FL-Incentivizer, which relies on custom smart contracts to maintain flow governance of the FL process in a transparent and immutable manner. FL-Incentivizer commercializes and tokenizes the global model using FL-NFT (FL Non-Fungible Token) based on the ERC-721 standard. FL-Incentivizer uses ERC-20 compliant FL-Tokens to incentivize devices participating in FL. We present the system design and operational sequence of the FL-Incentivizer. We provide implementation and deployment details, complete smart contract codes, and qualitative evaluation of the FL-Incentivizer. After implementing FL-Incentivizer for a global iteration of a Federated learning task, we showed the FL-NFT on OpenSea and an FL-Token for a learner on MetaMask. FL-NFTs can be traded on markets such as OpenSea like other NFTs. While FL-Tokens can be transferred in the same manner as other ERC-20-based tokens. |
| first_indexed | 2024-04-10T09:14:42Z |
| format | Article |
| id | doaj.art-cae05d923b064bc38be070c6ea108dc4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:14:42Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cae05d923b064bc38be070c6ea108dc42023-02-21T00:02:47ZengIEEEIEEE Access2169-35362023-01-01114381439910.1109/ACCESS.2023.323548410012365FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and TrainingUmer Majeed0https://orcid.org/0000-0002-5908-3889Latif U. Khan1https://orcid.org/0000-0002-7678-6949Sheikh Salman Hassan2https://orcid.org/0000-0002-5317-6494Zhu Han3https://orcid.org/0000-0002-6606-5822Choong Seon Hong4https://orcid.org/0000-0003-3484-7333Department of Computer Science and Engineering, Kyung Hee University, Yongin, South KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin, South KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin, South KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin, South KoreaDepartment of Computer Science and Engineering, Kyung Hee University, Yongin, South KoreaFederated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is challenging to claim ownership rights and commercialize the global model efficiently and transparently. Additionally, incentives need to be provided to ensure that devices participate in the FL process. In this paper, we propose a smart contract-based framework called FL-Incentivizer, which relies on custom smart contracts to maintain flow governance of the FL process in a transparent and immutable manner. FL-Incentivizer commercializes and tokenizes the global model using FL-NFT (FL Non-Fungible Token) based on the ERC-721 standard. FL-Incentivizer uses ERC-20 compliant FL-Tokens to incentivize devices participating in FL. We present the system design and operational sequence of the FL-Incentivizer. We provide implementation and deployment details, complete smart contract codes, and qualitative evaluation of the FL-Incentivizer. After implementing FL-Incentivizer for a global iteration of a Federated learning task, we showed the FL-NFT on OpenSea and an FL-Token for a learner on MetaMask. FL-NFTs can be traded on markets such as OpenSea like other NFTs. While FL-Tokens can be transferred in the same manner as other ERC-20-based tokens.https://ieeexplore.ieee.org/document/10012365/Federated learningEthereumsmart contractstokenNFTERC-20 |
| spellingShingle | Umer Majeed Latif U. Khan Sheikh Salman Hassan Zhu Han Choong Seon Hong FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training IEEE Access Federated learning Ethereum smart contracts token NFT ERC-20 |
| title | FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training |
| title_full | FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training |
| title_fullStr | FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training |
| title_full_unstemmed | FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training |
| title_short | FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training |
| title_sort | fl incentivizer fl nft and fl tokens for federated learning model trading and training |
| topic | Federated learning Ethereum smart contracts token NFT ERC-20 |
| url | https://ieeexplore.ieee.org/document/10012365/ |
| work_keys_str_mv | AT umermajeed flincentivizerflnftandfltokensforfederatedlearningmodeltradingandtraining AT latifukhan flincentivizerflnftandfltokensforfederatedlearningmodeltradingandtraining AT sheikhsalmanhassan flincentivizerflnftandfltokensforfederatedlearningmodeltradingandtraining AT zhuhan flincentivizerflnftandfltokensforfederatedlearningmodeltradingandtraining AT choongseonhong flincentivizerflnftandfltokensforfederatedlearningmodeltradingandtraining |