Abstract
In this paper, we propose blockchain network based architecture called 'FLchain' for enhancing security of Federated Learning (FL). We leverage the concept of channels for learning multiple global models on FLchain. Local model parameters for each global iteration are stored as a block on the channel-specific ledger. We introduce the notion of 'the global model state trie' which is stored and updated on the blockchain network based on the aggregation of local model updates collected from mobile devices. Qualitative evaluation shows that FLchain is more robust than traditional FL schemes as it ensures provenance and maintains auditable aspects of FL model in an immutable manner.
| Original language | English |
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| Title of host publication | 2019 20th Asia-Pacific Network Operations and Management Symposium |
| Subtitle of host publication | Management in a Cyber-Physical World, APNOMS 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9784885523205 |
| DOIs | |
| Publication status | Published - Sept 2019 |
| Event | 20th Asia-Pacific Network Operations and Management Symposium, APNOMS 2019 - Matsue, Japan Duration: 18 Sept 2019 → 20 Sept 2019 |
Publication series
| Name | 2019 20th Asia-Pacific Network Operations and Management Symposium: Management in a Cyber-Physical World, APNOMS 2019 |
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Conference
| Conference | 20th Asia-Pacific Network Operations and Management Symposium, APNOMS 2019 |
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| Country/Territory | Japan |
| City | Matsue |
| Period | 18/09/19 → 20/09/19 |
Bibliographical note
Publisher Copyright:© 2019 IEICE.
Keywords
- Blockchain
- distributed computing
- federated learning
- multi-access edge computing