  Federated Learning (FL) has been recently proposed as an emerging paradigm to
build machine learning models using distributed training datasets that are
locally stored and maintained on different devices in 5G networks while
providing privacy preservation for participants. In FL, the central aggregator
accumulates local updates uploaded by participants to update a global model.
However, there are two critical security threats: poisoning and membership
inference attacks. These attacks may be carried out by malicious or unreliable
participants, resulting in the construction failure of global models or privacy
leakage of FL models. Therefore, it is crucial for FL to develop security means
of defense. In this article, we propose a blockchain-based secure FL framework
to create smart contracts and prevent malicious or unreliable participants from
involving in FL. In doing so, the central aggregator recognizes malicious and
unreliable participants by automatically executing smart contracts to defend
against poisoning attacks. Further, we use local differential privacy
techniques to prevent membership inference attacks. Numerical results suggest
that the proposed framework can effectively deter poisoning and membership
inference attacks, thereby improving the security of FL in 5G networks.
