logL_entry: Aggregate Log-Likelihood Function for Federated Learning

In FedIRT: Federated Item Response Theory Models

Aggregate Log-Likelihood Function for Federated Learning

Description

Computes the sum of log-likelihoods across multiple schools in a federated learning setting. The function logL_entry aggregates contribution of each school's log-likelihood to the overall model. It is designed to be used within the optimization process of fedirt.

Usage

logL_entry(ps)

Arguments

ps	A parameter vector consisting of item parameters; it should include both discrimination (a) and difficulty (b) parameters.

Details

In a federated learning context, each school computes its log-likelihood locally. The logL_entry function is responsible for aggregating these values. Users are expected to provide an implementation for getlogL_from_index, which should include network requests to retrieve log-likelihoods calculated by each school, or for simplified prototyping purposes, could directly use a logL function to compute likelihoods locally.

Value

The sum of log-likelihoods as a single numeric value, representing the likelihood of the entire federated dataset under the current model's parameters.

FedIRT documentation built on Sept. 30, 2024, 9:34 a.m.