You can even have federated sites that have their own federation server. Such an "in-house" solution is usually faster than the federation server for a particular client, as it is closer to the client and is usually optimized to perform that particular task.
Web server federation is the least common deployment of federation. In this scenario, the "federation server" is a Web server, which, instead of being used to communicate directly with clients, serves to redirect clients to a backend service that in turn services the requests. Examples include Wicket, Struts, and Grails. This approach is useful when there is no appropriate federation server for the client, and when the organization or organization's Web server cannot be used as a federation server.
Service provider federation occurs when a single, central service provider hosts all the data and content of a system. For example, the content of a website is stored on the Web server of an Internet service provider. The Internet service provider is the "federation server" in this example.
A federated service architecture usually provides for the use of a federation server, and the architecture involves multiple parties, including service providers, federation server, and clients.
Federation can also refer to the ability to distribute information between organizations. In this sense, it is also referred to as "organization-to-organization information sharing", "organization-to-organization integration", or "organization to organization integration" (O2O).
See also
Federated learning
Federation model
Federation model (computer science)
Federation/open federation
Federation (disambiguation)
Open ID
Organizational identity
References
Further reading
External links
Search federation
OpenID Connect Federation Model
OpenID Connect Federation Tutorial
Category:Computer security
Category:Internet privacyQ:
Numpy array dtype weird
Here is a code:
import numpy as np
import pandas as pd
x=np.array([['a','a','b','b'],['c','c','d','d'],['e','e','f','f'],['g','g','h','h']])
y=pd.DataFrame(x)
y.columns=['C1','C2','C3','C4']
y.head()
The result is like this:
C1 C
Related links: