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.

翻译：标准的联邦学习（FL）方法容易受到搭便车困境的影响：参与方可以贡献极少甚至不贡献，却仍能获得训练良好的聚合模型。虽然已有机制尝试解决搭便车困境，但均未涉及真实性问题。在实践中，对抗性参与方可以向服务器提供虚假信息，以欺骗方式逃避对联邦训练的贡献。为了使抗搭便车的联邦机制具有真实性，从而在实践中更不易失效，我们提出了 FACT。FACT 是首个实现以下目标的联邦机制：(1) 通过惩罚系统消除联邦搭便车行为，(2) 通过创建竞争环境确保参与方提供真实信息，(3) 通过提供优于单独训练的性能来鼓励参与方参与。实验表明，当参与方不诚实时，FACT 能避免搭便车行为，并将参与方损失降低 4 倍以上。