Federated search retrieves information from a variety of sources via a search application built on top of one or more search engines. A user makes a single query request which is distributed to the search engines, databases or other query engines participating in the federation. The federated search then aggregates the results that are received from the search engines for presentation to the user. Federated search can be used to integrate disparate information resources within a single large organization ("enterprise") or for the entire web.

Federated search, unlike distributed search, requires centralized coordination of the searchable resources. This involves both coordination of the queries transmitted to the individual search engines and fusion of the search results returned by each of them.

Federated search came about to meet the need of searching multiple disparate content sources with one query. This allows a user to search multiple databases at once in real time, arrange the results from the various databases into a useful form and then present the results to the user.

As such, it is an information aggregation or integration approach - it provides single point access to many information resources, and typically returns the data in a standard or partially homogenized form. Other approaches include constructing an Enterprise data warehouse, Data lake, or Data hub. Federated Search queries many times in many ways (each source is queried separately) where other approaches import and transform data many times, typically in overnight batch processes. Federated search provides a real-time view of all sources (to the extent they are all online and available).

In industrial search engines, such as LinkedIn, federated search is used to personalize vertical preference for ambiguous queries. For instance, when a user issues a query like "machine learning" on LinkedIn, he or she could mean to search for people with machine learning skill, jobs requiring machine learning skill or content about the topic. In such cases, federated search could exploit user intent (e.g., hiring, job seeking or content consuming) to personalize the vertical order for each individual user.

As described by Peter Jacso, federated searching consists of

Federated search portals, either commercial or open access, generally search public access bibliographic databases, public access Web-based library catalogues (OPACs), Web-based search engines like Google and/or open-access, government-operated or corporate data collections. These individual information sources send back to the portal's interface a list of results from the search query. The user can review this hit list. Some portals will merely screen scrape the actual database results and not directly allow a user to enter the information source's application. More sophisticated ones will de-dupe the results list by merging and removing duplicates. There are additional features available in many portals, but the basic idea is the same: to improve the accuracy and relevance of individual searches as well as reduce the amount of time required to search for resources.

This process allows federated search some key advantages when compared with existing crawler-based search engines. Federated search need not place any requirements or burdens on owners of the individual information sources, other than handling increased traffic. Federated searches are inherently as current as the individual information sources, as they are searched in real time.