A Proof of Useful Work for Artificial Intelligence on the Blockchain
January 25, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andrei Lihu, Jincheng Du, Igor Barjaktarevic, Patrick Gerzanics, Mark Harvilla
arXiv ID
2001.09244
Category
cs.DC: Distributed Computing
Cross-listed
cs.LG
Citations
36
Venue
arXiv.org
Last Checked
6 months ago
Abstract
Bitcoin mining is a wasteful and resource-intensive process. To add a block of transactions to the blockchain, miners spend a considerable amount of energy. The Bitcoin protocol, named 'proof of work' (PoW), resembles a lottery and the underlying computational work is not useful otherwise. In this paper, we describe a novel 'proof of useful work' (PoUW) protocol based on training a machine learning model on the blockchain. Miners get a chance to create new coins after performing honest ML training work. Clients submit tasks and pay all training contributors. This is an extra incentive to participate in the network because the system does not rely only on the lottery procedure. Using our consensus protocol, interested parties can order, complete, and verify useful work in a distributed environment. We outline mechanisms to reward useful work and punish malicious actors. We aim to build better AI systems using the security of the blockchain.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Distributed Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger
R.I.P.
π»
Ghosted
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
R.I.P.
π»
Ghosted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
R.I.P.
π»
Ghosted
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
R.I.P.
π»
Ghosted
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted