Unlearning or Not: A Strategic Data Forgetting Scheme for Federated Unlearning With Bounded Rationality

Federated unlearning (FUL, a process that removes a user's data influence from machine learning models trained across multiple computers) helps protect privacy by letting users exercise their right to be forgotten. This paper proposes a new framework where an FL server uses game theory (a mathematical approach to modeling strategic decision-making) and prospect theory (a model of how people make decisions under uncertainty) to incentivize clients to keep more data during unlearning while preventing selfish behavior.