  Federated Learning (FL) is a machine learning technique that addresses the
privacy challenges in terms of access rights of local datasets by enabling the
training of a model across nodes holding their data samples locally. To achieve
decentralized federated learning, blockchain-based FL was proposed as a
distributed FL architecture. In decentralized FL, the chief is eliminated from
the learning process as workers collaborate between each other to train the
global model. Decentralized FL applications need to account for the additional
delay incurred by blockchain-based FL deployments. Particularly in this
setting, to detect targeted/untargeted poisoning attacks, we investigate the
end-to-end learning completion latency of a realistic decentralized FL process
protected against poisoning attacks. We propose a technique which consists in
decoupling the monitoring phase from the detection phase in defenses against
poisoning attacks in a decentralized federated learning deployment that aim at
monitoring the behavior of the workers. We demonstrate that our proposed
blockchain-based monitoring improved network scalability, robustness and time
efficiency. The parallelization of operations results in minimized latency over
the end-to-end communication, computation, and consensus delays incurred during
the FL and blockchain operations.
