  Federated Learning (FL) is a distributed, and decentralized machine learning
protocol. By executing FL, a set of agents can jointly train a model without
sharing their datasets with each other, or a third-party. This makes FL
particularly suitable for settings where data privacy is desired.
  At the same time, concealing training data gives attackers an opportunity to
inject backdoors into the trained model. It has been shown that an attacker can
inject backdoors to the trained model during FL, and then can leverage the
backdoor to make the model misclassify later. Several works tried to alleviate
this threat by designing robust aggregation functions. However, given more
sophisticated attacks are developed over time, which by-pass the existing
defenses, we approach this problem from a complementary angle in this work.
Particularly, we aim to discourage backdoor attacks by detecting, and punishing
the attackers, possibly after the end of training phase.
  To this end, we develop a hybrid blockchain-based FL framework that uses
smart contracts to automatically detect, and punish the attackers via monetary
penalties. Our framework is general in the sense that, any aggregation
function, and any attacker detection algorithm can be plugged into it. We
conduct experiments to demonstrate that our framework preserves the
communication-efficient nature of FL, and provide empirical results to
illustrate that it can successfully penalize attackers by leveraging our novel
attacker detection algorithm.
