Towards blockchain-based federated machine learning: smart contract for model inference
| Autor: | Evaldas Vaiciukynas, Vaidotas Drungilas, Lina Čeponienė, Mantas Jurgelaitis, Rita Butkienė |
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| Přispěvatelé: | MDPI AG (Basel, Switzerland) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Service (systems architecture)
runtime benchmarking model validation Blockchain Smart contract Computer science 02 engineering and technology Microservices Machine learning computer.software_genre lcsh:Technology Oracle chaincode lcsh:Chemistry microservices system architectures 0202 electrical engineering electronic engineering information engineering Overhead (computing) General Materials Science Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes distributed ledger business.industry lcsh:T Process Chemistry and Technology General Engineering oracle service 020206 networking & telecommunications Usability lcsh:QC1-999 Computer Science Applications machine learning lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 020201 artificial intelligence & image processing Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) computer run-time benchmarking lcsh:Physics Curse of dimensionality |
| Zdroj: | Applied Sciences, Vol 11, Iss 1010, p 1010 (2021) Applied Sciences Volume 11 Issue 3 |
| Popis: | Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set of supported models by introducing the oracle service and exploring the usability of blockchain-based architecture. The investigated architecture combines an oracle service with a Hyperledger Fabric chaincode. We compared two logistic regression implementations in Go language&mdash a pure chaincode and an oracle service&mdash at various data (2&ndash 32 k instances) and network (3&ndash 13 peers) sizes. Experiments were run to assess the performance of blockchain-based model inference using 2D synthetic and EEG eye state datasets for a supervised machine learning detection task. The benchmarking results showed that the impact on performance is acceptable with the median overhead of oracle service reaching 2&ndash 4%, depending on the dimensionality of the dataset. The overhead tends to diminish at large dataset sizes with the runtime depending on the network size linearly, where additional peers increased the runtime by 6.3 and 6.6 s for 2D and EEG datasets, respectively. Demonstrated negligible difference between implementations justifies the flexible choice of model in the blockchain-based federated learning and other machine learning applications. |
| Databáze: | OpenAIRE |
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