Can you talk about this in the end? Or do you constantly send people to the official website?
And why can not people find information on their own? I believe that this is not so difficult
Topic: [ANN] Decentralized Machine Learning - page 40. (Read 6229 times)
Can you talk about this in the end? Or do you constantly send people to the official website?
All because for the transfer of reports between user devices it is not necessary to spend a lot of traffic, but each modern phone has sufficient characteristics to solve the algorithm
It's right! But it's all about the operating system of a mobile device, I believe. Write a program and firmware for the Android operating system - it's easy, and the rest will need to look for an approach ..All because for the transfer of reports between user devices it is not necessary to spend a lot of traffic, but each modern phone has sufficient characteristics to solve the algorithm
It's right! But it's all about the operating system of a mobile device, I believe. Write a program and firmware for the Android operating system - it's easy, and the rest will need to look for an approach ..
All because for the transfer of reports between user devices it is not necessary to spend a lot of traffic, but each modern phone has sufficient characteristics to solve the algorithm
It's right! But it's all about the operating system of a mobile device, I believe. Write a program and firmware for the Android operating system - it's easy, and the rest will need to look for an approach ..Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Centralization of Processing Power
Nowadays, machine learning is mainly conducted through a centralized computer,
which its processing power is usually limited or confined to the processors of a
single machine.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Centralization of Processing Power
Nowadays, machine learning is mainly conducted through a centralized computer,
which its processing power is usually limited or confined to the processors of a
single machine.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
Inaccessibility of Private DataTraditional machine learning requires datasets to be uploaded to a dedicated server.
Due to privacy concern, massive amount of private data stored in individual devices
is untapped.
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
No, this will not happen, since you will not send photos in ordinary form to anyone. This has already been written many times on the forum and spoken in white paper
Yeah, the main thing is for the development team to take care of the security of my data. It will be very insulting if their system does not survive the simplest attack and all the data that was passed on to the client will fall into the wrong hands
There are few types of DML Protocol participants. How will each of the participant types use the test?
Customers
Companies, research institutions, governments or non-governmental organizations
request machine learning algorithms for prediction reports.
Algorithm Developers
Create algorithms and request for fine-tuning of algorithms. Algorithm creations
are decentralized and crowdsourced in developer community.
Layers of Decentralized Nodes
Distributing Nodes deliver algorithms, Federated Nodes aggregate results,
Report Nodes produce reports and Algo Refining Nodes improve algorithms.
4 Data Owners
Contribute idle processing power and allow access of private datasets of their
devices for local prediction results, participate in fine-tuning of algorithms.
I think that everyone will use this product the way it is prescribed to them. That is, for example, buyers will give requests and receive answers, analyze themCustomers
Companies, research institutions, governments or non-governmental organizations
request machine learning algorithms for prediction reports.
Algorithm Developers
Create algorithms and request for fine-tuning of algorithms. Algorithm creations
are decentralized and crowdsourced in developer community.
Layers of Decentralized Nodes
Distributing Nodes deliver algorithms, Federated Nodes aggregate results,
Report Nodes produce reports and Algo Refining Nodes improve algorithms.
4 Data Owners
Contribute idle processing power and allow access of private datasets of their
devices for local prediction results, participate in fine-tuning of algorithms.
There are few types of DML Protocol participants. How will each of the participant types use the test?
Customers
Companies, research institutions, governments or non-governmental organizations
request machine learning algorithms for prediction reports.
Algorithm Developers
Create algorithms and request for fine-tuning of algorithms. Algorithm creations
are decentralized and crowdsourced in developer community.
Layers of Decentralized Nodes
Distributing Nodes deliver algorithms, Federated Nodes aggregate results,
Report Nodes produce reports and Algo Refining Nodes improve algorithms.
4 Data Owners
Contribute idle processing power and allow access of private datasets of their
devices for local prediction results, participate in fine-tuning of algorithms.
I think that everyone will use this product the way it is prescribed to them. That is, for example, buyers will give requests and receive answers, analyze them Customers
Companies, research institutions, governments or non-governmental organizations
request machine learning algorithms for prediction reports.
Algorithm Developers
Create algorithms and request for fine-tuning of algorithms. Algorithm creations
are decentralized and crowdsourced in developer community.
Layers of Decentralized Nodes
Distributing Nodes deliver algorithms, Federated Nodes aggregate results,
Report Nodes produce reports and Algo Refining Nodes improve algorithms.
4 Data Owners
Contribute idle processing power and allow access of private datasets of their
devices for local prediction results, participate in fine-tuning of algorithms.
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