Rich client

In computer networking, a rich client (also called heavy, fat or thick client) is a computer (a "client" in client–server network architecture) that typically provides rich functionality independent of the central server. This kind of computer was originally known as just a "client" or "thick client," in contrast with "thin client", which describes a computer heavily dependent on a server's applications. A rich client may be described as having a rich user interaction. While a rich client still requires at least periodic connection to a network or central server , it is often characterised by the ability to perform many functions without a connection. In contrast, a thin client generally does as little processing as possible on the client, relying on access to the server each time input data needs to be processed or validated. The designer of a client–server application decides which parts of the task should be executed on the client, and which on the server. This decision can crucially affect the cost of clients and servers, the robustness and security of the application as a whole, and the flexibility of the design to later modification or porting. The characteristics of the user interface often force the decision on a designer. For instance, a drawing package could require download of an initial image from a server, and allow all edits to be made locally, returning the revised drawing to the server upon completion. This would require a rich client and might be characterised by a long delay to start and stop (while a whole complex drawing was transferred), but quick to edit. Conversely, a thin client could download just the visible parts of the drawing at the beginning and send each change back to the server to update the drawing. This might be characterised by a short start-up time, but a tediously slow editing process. The original server clients were simple text display terminals including Wyse VDUs, and rich clients were generally not used until the increase in PC usage.

Privatized graph federated learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

SPRINGER2023

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Privatized graph federated learning

Privatized graph federated learning

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis