Difference between revisions of "Federated Learning"
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==Privacy Enhancing== | ==Privacy Enhancing== | ||
| − | + | To make the graph privacy-enhancing we demand that any personally identifiable information (PII) is restricted to the leaves. | |
==References== | ==References== | ||
[[Category: Artificial Intelligence]] | [[Category: Artificial Intelligence]] | ||
Revision as of 18:08, 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.
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.
