Methods and systems for a synchronized distributed data structure for federated machine learning

1. A system for a synchronized distributed data structure for federated machine learning, the system including:

a computing device, the computing device configured to:

receive an input from a remote device;

parse the input to identify protected and non-protected data contained within the input using a language processing module, wherein the language processing module is configured to:

extract one or more words from the input; and

produce mathematical associations between the one or more words extracted;

transform the non-protected data into an encrypted proof-linked assertion;

convert the protected data into an encrypted datastore;

insert the encrypted proof-linked assertion into a hashed field of an immutable sequential data structure;

retrieve the input from at least one of the immutable sequential data structure and the encrypted datastore;

generate a record as a function of the input;

perform a first machine-learning process as a function of the input, wherein the first machine-learning process is configured to determine typical transaction values for a set of participants using training data correlating transaction parameters to transaction values;

calculate an incentive for each participant type in a set of participants in a given transaction as a function of the typical transaction values;

generate a sub-listing of the record as a function of contributions received from the set of participants as a function of the calculated incentive; and

store the sub-listing of the record in the immutable sequential data structure;

wherein the computing device is further configured to:

receive a parameter list data input;

analyze the parameter list data input to create fragments of the parameter list data input, wherein the fragments include a first fragment containing information on a drug, a second fragment containing information on a date the drug was filled at a pharmacy, and a third fragment containing information on a treatment the drug was utilized for;

perform a second machine-learning process as a function of the fragments of the parameter list data input; and

generate a second record utilizing the second machine-learning process.

2. The system of claim 1 , wherein the computing device is further configured to:

retrieve a first encrypted proof-linked assertion relating to a first user;

retrieve a second encrypted proof-linked assertion relating to a second user;

execute a third record between the first user and the second user as a function of the first encrypted proof-linked assertion and the second encrypted proof-linked assertion; and

post the executed third record to the immutable sequential data structure.

3. The system of claim 1 , wherein the computing device is further configured to transform the non-protected data into an encrypted proof-linked assertion further comprises generating an encrypted proof using elliptic curve cryptography.

4. The system of claim 1 , wherein the computing device is further configured to:

transmit the second record to a plurality of outposts;

receive a communication from the plurality of outposts;

transmit the communication from the plurality of outposts to a remote device; and

receive an assignation referencing the communication from the plurality of outposts from the remote device.

5. The system of claim 1 , wherein the computing device is further configured to:

receive an input containing a requested treatment;

retrieve from an immutable sequential data structure a numerical parameter for the requested treatment; and

locate a treatment as a function of the numerical parameter using a fourth machine-learning process.

6. The system of claim 5 , wherein the computing device is further configured to:

retrieve an element of user data stored within a processing module; and

generate the input containing the requested treatment using the element of user data and a fifth machine-learning process.

7. The system of claim 1 , wherein the computing device is further configured to:

identify a fourth record including a first biochemical identification;

receive an input negating the first biochemical identification; and

generate a second biochemical identification using a sixth machine-learning process.

8. The system of claim 7 , wherein the computing device is further configured to:

receive an authentication of the second biochemical identification; and

create a fifth record wherein the fifth record contains an indication of the second biochemical identification.

9. The system of claim 1 , wherein the machine-learning process further comprises a federated machine-learning process.

10. The system of claim 1 , wherein parsing the input to identify protected and non-protected data contained within the input further comprises using a language processing model configured to produce associations between a plurality of terms extracted from a corpus of documents and detect associations between such terms.

11. A method of implementing a synchronized distributed data structure for federated machine learning, the method comprising:

receiving, by a computing device, an input from a remote device;

parsing, by the computing device, the input to identify protected and non-protected data contained within the input using a language processing module, wherein using the language processing module further comprises:

extracting one or more words from the input; and

producing mathematical associations between the one or more words extracted;

generating, by the computing device, an encrypted proof-linked assertion using the non-protected data;

encrypting, by the computing device, the protected data;

inserting, by the computing device, the encrypted proof-linked assertion into a hashed field of an immutable sequential data structure;

retrieving, by the computing device, the input from at least one of the immutable sequential data structure and the encrypted data;

generating, by the computing device, a record as a function of the input;

performing, by the computing device, a first machine-learning process as a function of the input, wherein the first machine-learning process is configured to determine typical transaction values for a set of participants using training data correlating transaction parameters to transaction values;

calculating an incentive for each participant type in a set of participants in a given transaction as a function of the typical transaction values;

generating a sub-listing of the record as a function of contributions received from the set of participants as a function of the calculated incentive;

storing the sub-listing of the record in the immutable sequential data structure;

receiving, by the computing device, a parameter list data input;

analyzing, by the computing device, the parameter list data input to create fragments of the parameter list data input, wherein the fragments include a first fragment containing information on a drug, a second fragment containing information on a date the drug was filled at a pharmacy, and a third fragment containing information on a treatment the drug was utilized for;

performing, by the computing device, a second machine-learning process as a function of the fragments of the parameter list data input; and

generating, by the computing device, a second record utilizing the second machine-learning process.

12. The method of claim 11 further comprising:

executing a third record between a first user and a second user as a function of a first encrypted proof-linked assertion and a second encrypted proof-linked assertion; and

inserting the executed third record in a hashed entry of the immutable sequential data structure.

13. The method of claim 11 , wherein generating the encrypted proof-linked assertion further comprises generating an encrypted proof using elliptic curve cryptography.

14. The method of claim 11 further comprising:

transmitting the second record to a plurality of outposts;

receiving a communication from the plurality of outposts;

transmitting the communication from the plurality of outposts to a remote device; and

receiving an assignation referencing the communication from the plurality of outposts from the remote device.

15. The method of claim 11 further comprising:

receiving a first input;

retrieving from an immutable sequential data structure a numerical parameter for the input; and

locating a second input as a function of the numerical parameter using a fourth machine-learning process.

16. The method of claim 15 further comprising:

retrieving an element of user data stored within a processing module; and

generating the input containing the second input using the element of user data and a fifth machine-learning process.

17. The method of claim 11 , wherein the machine-learning process further comprises a federated machine-learning process.

18. The method of claim 11 , wherein parsing the input to identify protected and non-protected data contained within the input further comprises using a language processing model configured to produce associations between a plurality of terms extracted from a corpus of documents and detect associations between such terms.