# Federated Covariate Shift Adaptation for Missing Target Output Values

## TL;DR

This paper extends a covariate shift adaptation algorithm to federated learning, proposing a new method for handling missing target outputs that is both theoretically sound and empirically validated.

## Contribution

It introduces a federated covariate shift adaptation algorithm with an unbiased target risk estimate suitable for data islands in federated learning.

## Key findings

- The proposed method is asymptotically unbiased.
- The algorithm demonstrates superior performance in experiments.
- Theoretical analysis confirms its desirable variance properties.

## Abstract

The most recent multi-source covariate shift algorithm is an efficient hyperparameter optimization algorithm for missing target output. In this paper, we extend this algorithm to the framework of federated learning. For data islands in federated learning and covariate shift adaptation, we propose the federated domain adaptation estimate of the target risk which is asymptotically unbiased with a desirable asymptotic variance property. We construct a weighted model for the target task and propose the federated covariate shift adaptation algorithm which works preferably in our setting. The efficacy of our method is justified both theoretically and empirically.