BlogPGWAD Stake-Pool & DeepchainAda

What is PGWAD and what is the DeepchainAda project?

PGWAD

What is PGWAD and DeepchainAda?

PGWAD is a cardano stakepool running on . PGWAD is part of the armada-alliance. This is an alliance of independent stake pool operators using low powered ARM cores to help decentralize Cardano.
PGWAD means Packet GateWay for AI and Decentralization. PGWAD has been focusing on the DeepchainAda project. This short blog and video below explain the project and future plans.
Recently there has been a lot of talk about AI models showing bias. For example some of the healthcare related models were biased while suggesting treatments. One of the root causes of this is lack of data. Usually many projects cannot share data because of Privacy issues. So models get trained with limited data.
One advancement that happened was the concept of federated learning or distributed learning. In this there is a central server and model gets trained where data is and only model parameters are send to a central sever which aggregates this.
Blockchain like Cardano can help decentralize this federated learning. Along with this there are also concepts of Split Neural Nets that help in training. Multiple papers have talked about this and people have implemented this in mainly an experimental chain or some in Eth. So DeepchainAda focuses on building this distributed learning framework on Cardano. The idea is to use the Smart Contracts to orchestrate these tasks. Couple this with , the model can be enabled to work in the decentralized marketplace along with other AI models.
So in short the DeepchainAda project focuses on getting the Plutus Smart Contracts to help Distributed Machine Learning and interface the model with SingularityNet Framework using the AI DSL (Idris). The aim is to also enable (distributed computing) incase data provider does not have compute. All of this should be achieved in such a way that Privacy and Auditability is preserved. All the reference papers are updated in my video descriptions.
Last updated on 3 years ago
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