  Unbiased Learning to Rank (ULTR) studies the problem of learning a ranking
function based on biased user interactions. In this framework, ULTR algorithms
have to rely on a large amount of user data that are collected, stored, and
aggregated by central servers.
  In this paper, we consider an on-device search setting, where users search
against their personal corpora on their local devices, and the goal is to learn
a ranking function from biased user interactions. Due to privacy constraints,
users' queries, personal documents, results lists, and raw interaction data
will not leave their devices, and ULTR has to be carried out via Federated
Learning (FL).
  Directly applying existing ULTR algorithms on users' devices could suffer
from insufficient training data due to the limited amount of local
interactions. To address this problem, we propose the FedIPS algorithm, which
learns from user interactions on-device under the coordination of a central
server and uses click propensities to remove the position bias in user
interactions. Our evaluation of FedIPS on the Yahoo and Istella datasets shows
that FedIPS is robust over a range of position biases.
