From f11d3cbe1bef0dff49ed9348c3611a6b2b5f51e4 Mon Sep 17 00:00:00 2001 From: Ernih <71947919+Ernih@users.noreply.github.com> Date: Wed, 15 Nov 2023 13:16:13 +0100 Subject: [PATCH] Update applications/Deitos_Network.md Co-authored-by: S E R A Y A --- applications/Deitos_Network.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/applications/Deitos_Network.md b/applications/Deitos_Network.md index be816bdea34..7fbc4fa6c02 100644 --- a/applications/Deitos_Network.md +++ b/applications/Deitos_Network.md @@ -206,7 +206,7 @@ While Deitos Network shares similarities with platforms such as Crust, Arweave, - **DecentralML**: A Polkadot protocol for decentralised federated machine learning and collective governance. -Based on the grant information from DecentralML, it appears there are parallels in terms of decentralizing machine learning model training, where rewards are based on data model training contributions and parameter adjustments by governance. +Based on the grant information from [DecentralML](https://github.com/w3f/Grants-Program/blob/master/applications/decentral_ml.md), it appears there are parallels in terms of decentralizing machine learning model training, where rewards are based on data model training contributions and parameter adjustments by governance. Deitos approach, however, adopts a distinct architecture and game theory strategy. It focuses on infrastructure providers offering private services, competing to deliver optimal solutions to consumers. In future developments, these providers may also engage in maintaining and utilizing a shared public dataset, rewarded for hosting this data and processing consumer requests. (Section added from application's feedback).