Home > ICOs > Decentralized Machine Learning

Overview

Unleash untapped private data, idle processing power and crowdsourced algorithms

Project stage

Prototype

Detailed description

Decentralized Machine Learning (DML) protocol is designed to expand the reach to untapped private data and unleash their potential to facilitate machine learning development while providing economic incentives and protecting data privacy. Machine learning algorithm will be run on the devices without extracting the data from the devices, which will be kept within the devices. Only the machine learning result will be aggregated with outcomes generated from other devices to form an unbiased, comprehensive and accurate crowdsourced analytics and predictions. Through DML protocol, both the private data and processing power for machine learning are decentralized as algorithms are run directly on individual devices by utilizing their idle processing power.

Problem

Often only the large corporations can afford investing huge initial capital and resources to build in-house machine learning algorithms or acquire tailor-made ones from consultancy firms and apply in their own businesses. Therefore, machine learning applications are often only found in some large international corporations rather than local or medium sizes companies.

Solution

DML aims to create a decentralized machine learning protocol and ecosystem, where customers, such corporate customers, research institutions, government and non-government organizations or even individuals, who wish to run analytical predictions can acquire appropriate algorithms from crowdsourced developers through the DML marketplace. 

With the aid of DML protocol, the machine learning algorithms can be run on the untapped private data and leverage the idle processing power of individual devices resulting in more precise predictions. Furthermore, the developers can improve their algorithm and its predictability by the crowdsourced model trainers in the DML protocol.

Features

On-Device Machine Learning without Data Extraction
Utilize untapped private data in individual devices for machine learning with privacy protected

Unleash Innovation from Periphery
Unlock innovation by creating a developer community and competitions in algorithm marketplace

Connect Idle Processing Power of Billions Devices
Leverage processing power of all connected devices for running machine learning algorithms

Mass Participation in Machine Learning Training
Create an algorithm trainer community to improve algorithms through collective efforts

Multi-Blockchain Adoption and Interoperability
Communicate across multi-chains to achieve blockchain-agnostic protocol

Return Power to Ecosystem Owners
Avoid centralization and control by a few oligopolies via blockchain and tokenization

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Additional links

  • Token details

    • Token symbol ? Token symbol — a shorten token name. It is used during an ICO and after the coin listing at the cryptocurrency exchanges. : DML
    • Fundrasing target ? Fundraising target — the maximum amount of funds to be raised during an ICO. When it is reached, the developers stop selling the tokens because they do not need to raise more money for the project development. : 28,000 ETH
    • Token type ? Token type — a platform for a startup launch that influences the stability of blockchain operation, the speed of transactions and the fees. :Ethereum (ERC20)
    • Soft cap ? Soft cap — the minimum amount of funds to be raised for the project development. Sometimes when the soft cap has not been reached, the money is returned to the participants. : NA
    • Role of token ? Role of Token — type of token depending on the opportunities it offers to its owner. Utility tokens give their owners a right to use the project services, security tokens are aimed at bringing profit, and currency tokens are a money substitute. :Utility token
    • Total supply ? Total supply — a total amount of tokens that will be released by the developers. :330,000,000 DML
    • Escrow agent ? Escrow agent — a qualified agent who has the right of signature in a multisig wallet. An escrow agent participates in an ICO, monitors the financial operations of the developers and confirms their fairness. :No
    • Tokens for sale ? Tokens for sale — the number of tokens offered to the participants of an ICO. :118,800,000 DML
    • Whitelist ? Whitelist — a list of participants, who get an opportunity to buy tokens. To be whitelisted, you need to register on time because the number of participants and the registration period are usually limited. :Whitelist Closed
    • Additional emission ? Additional emission — an additional release of tokens. It can be done once after the crowd sale or on an ongoing basis. In the projects with a limited emission there is no additional emission. :No
    • Exchange listing ? Exchange listing — an assumed date when the token will be listed at a cryptocurrency exchange. The developers usually indicate it in a roadmap and a white paper. :NA
    • Accepting currencies ? Accepting currencies — cryptocurrencies and fiat currencies that can be used for buying the project tokens. :ETH,
    • Can't participate ? Can't participate — the countries where it is prohibited to buy tokens. These can be countries where ICOs are prohibited altogether, or countries that have the requirements that a particular project does not meet. :China, Hong Kong, Taiwan, USA,
    • Know Your Customer (KYC) ? Know Your Customer — a verification procedure for ICO participants, during which the developers can ask for personal data, a photo and a scanned copy of a passport of a potential investor. :Yes
    Get details
  • Token and Funds Distribution

    Token distribution date

    NA

    Unsold tokens

    NA
      • Token Distribution

        • Funds Distribution

      Sale schedule

      Round Token Price Bonus Min / Max Purchase Soft Cap Hard Cap
      Public sale — Ended
      16 Apr 00:00 UTC
      22 Apr 00:00 UTC
      0.17 USD 10% - Uncapped 28,000 ETH
      Public sale

      10% for early whitelist participants

      Read more about vesting and bonus
      • Team

        • Victor Cheung photo
          Victor Cheung
          Blockchain Developer
        • Michael Kwok photo
          Michael Kwok
          Project Lead Director
        • Jacky Chan photo
          Jacky Chan
          Blockchain and Software Developer
        • Wilson Lau photo
          Wilson Lau
          Machine Learning Engineer
        • Fabrice Fischer photo
          Fabrice Fischer
          Business Advisor
      • Advisors

        • Pascal Lejolif
          Machine Learning Advisor
        • Guillaume Huet
          Big Data / Machine Learning Advisor
        • Michael Edesess
          Machine Learning Advisor
        • Roderik van der Graaf
          Blockchain Advisor
        • Kyle Wong
          Machine Learning Advisor
        • Scott Christensen
          Machine Learning Advisor
        • Steven Cody Reynolds
          Blockchain Advisor
        • Matthew Slipper
          Machine Learning Advisor
        • Jesmer Wong
          Machine Learning Advisor
        • Eugene Tay
          PR & Marketing Advisor
        • Eric Byron
          Business Advisor

      Roadmap

      • Feb 2016

        Google published the research paper on federated learning

      • Mar 2016

        AlphaGo beat Lee Sedol in Go

      • Mar 2017

        Idea generation of decentralization in machine learning

      • Apr 2017

        Google published research blog in federated learning

      • May 2017

        Development of proof of concept

      • Sep 2017

        Idea generation of decentralization in algorithms

      • Dec 2017

        Whitepaper published and DecentralizedML.com online

      • Feb 2018

        Release of DML Protocol Gen 0 (DML Algo Marketplace) Prototype

      • Apr-May 2018
        • Token Generation Event and Launch of DML Protocol Gen 0 (DML Algo Marketplace) Beta
        • DML Algo Marketplace online
      • Jun 2018

        Research of state channels for increasing DML scalability

      • Jul-Aug 2018
        • First DML Algo competition to grow and support developers’ community
        • Release of DML Protocol Gen 1 beta
      • Dec 2018

        Release of customized state channels for increasing DML scalability

      • Q2 2019

        Release of DML Protocol Gen 2 beta (decentralized machine learning on-device private data with third-party service and data access)

      • Jul-Aug 2019

        Release of DML Protocol Gen 3 beta (decentralized machine learning on-device private data with third-party service and data access and mobile sensors/ IoT connection capability)

      • Sep-Oct 2019

        DML Protocol Gen 3 online

      • Q4 2019

        Research of general purpose API start for expanding usage of DML marketplace from machine learning to general applications

      • Q1 2020

        Research of new blockchain supporting mass adaption of general purpose decentralized applications and data privacy

      • Q2-Q3 2020

        elease of DML Protocol Gen 4 beta (Support deployment of general applications)

      • Q4 2020

        DML Protocol Gen 4 online

      Activity