Difference between revisions of "Federated Learning"

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(Privacy Enhancing)
(Context)
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==Context==
 
==Context==
Most human learning is federated in the sense that each human operates as an independent entity which receives inputs and creates outputs.
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* Most human learning is federated in the sense that each human operates as an independent entity which receives inputs and creates outputs.
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* In this pattern we model the human tendency to spread processing to each node with a similar hierarchy of capability among the nodes.
  
 
==A Hierarchical Directed Graph==
 
==A Hierarchical Directed Graph==

Revision as of 17:11, 2 September 2022

Full Title

A means of learning where the nodes can operate independently to create a common understanding of a problem.


Context

  • Most human learning is federated in the sense that each human operates as an independent entity which receives inputs and creates outputs.
  • In this pattern we model the human tendency to spread processing to each node with a similar hierarchy of capability among the nodes.

A Hierarchical Directed Graph

One solution is to create a network of all nodes that run any learning algorithm into a tree with paths that always move towards the root and away from the leaves as well as paths that go from the root out to the leaves.

Privacy Enhancing

To make the graph privacy-enhancing we demand that any personally identifiable information (PII) is restricted to the leaves.

References