  In current deep learning paradigms, local training or the Standalone
framework tends to result in overfitting and thus poor generalizability. This
problem can be addressed by Distributed or Federated Learning (FL) that
leverages a parameter server to aggregate model updates from individual
participants. However, most existing Distributed or FL frameworks have
overlooked an important aspect of participation: collaborative fairness. In
particular, all participants can receive the same or similar models, regardless
of their contributions. To address this issue, we investigate the collaborative
fairness in FL, and propose a novel Collaborative Fair Federated Learning
(CFFL) framework which utilizes reputation to enforce participants to converge
to different models, thus achieving fairness without compromising the
predictive performance. Extensive experiments on benchmark datasets demonstrate
that CFFL achieves high fairness, delivers comparable accuracy to the
Distributed framework, and outperforms the Standalone framework.
