Tangle Ledger for Decentralized Learning
Abstract
Federated learning has the potential to make machine learning applicable to highly privacy-sensitive domains and distributed datasets. In some scenarios, however, a central server for aggregating the partial learning results is not available. In fully decentralized learning, a network of peer-to-peer nodes collaborates to form a consensus on a global model without a trusted aggregating party. Often, the network consists of Internet of Things (IoT) and Edge computing nodes.Previous approaches for decentralized learning map the gradient batching and averaging algorithm from traditional federated learning to blockchain architectures. In an open network of participating nodes, the threat of adversarial nodes introducing poisoned models into the network increases compared to a federated learning scenario which is controlled by a single authority. Hence, the decentralized architecture must additionally include a machine learning-aware fault tolerance mechanism to address the increased attack surface.We propose a tangle architecture for decentralized learning, where the validity of model updates is checked as part of the basic consensus. We provide an experimental evaluation of the proposed architecture, showing that it performs well in both model convergence and model poisoning protection.
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Attacks such as poisoning attacks and data privacy threats can make FLchain systems vulnerable and risky, especially in distributed edge networks consisting of multiple devices and servers.
“Attacks such as poisoning attacks [54] and data privacy threats can make FLchain systems vulnerable and risky especially in distributed edge networks consisting of multiple devices and servers.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [54]
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- Authors
- Schmid, R., Pfitzner, B., Beilharz, J., Arnrich, B., & Polze, A.
- Affiliations
- Hasso Plattner Institute (DE)
- Venue
- 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 852-859
- ISSN
- 2164-7062 (ISSN-L)
- OpenAlex
- W3046868764
- Cited by
- 17
- References
- 27
- Citations by year
- 2025: 4 · 2024: 1 · 2023: 4 · 2022: 4 · 2021: 4
- FWCI
- 1.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Keywords
- decentralized learning, federated learning, model poisoning, consensus, fault tolerance, Internet of Things, edge computing, peer-to-peer networks, privacy-preserving machine learning
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