  Standard federated learning (FL) approaches are vulnerable to the free-rider
dilemma: participating agents can contribute little to nothing yet receive a
well-trained aggregated model. While prior mechanisms attempt to solve the
free-rider dilemma, none have addressed the issue of truthfulness. In practice,
adversarial agents can provide false information to the server in order to
cheat its way out of contributing to federated training. In an effort to make
free-riding-averse federated mechanisms truthful, and consequently less prone
to breaking down in practice, we propose FACT. FACT is the first federated
mechanism that: (1) eliminates federated free riding by using a penalty system,
(2) ensures agents provide truthful information by creating a competitive
environment, and (3) encourages agent participation by offering better
performance than training alone. Empirically, FACT avoids free-riding when
agents are untruthful, and reduces agent loss by over 4x.
