  The concept of Federated Learning (FL) has emerged as a convergence of
machine learning, information, and communication technology. It is vital to the
development of machine learning, which is expected to be fully decentralized,
privacy-preserving, secure, and robust. However, general federated learning
settings with a central server can't meet requirements in decentralized
environment. In this paper, we propose a decentralized, secure and
privacy-preserving global model training protocol, named PPT, for federated
learning in Peer-to-peer (P2P) Networks. PPT uses a one-hop communication form
to aggregate local model update parameters and adopts the symmetric
cryptosystem to ensure security. It is worth mentioning that PPT modifies the
Eschenauer-Gligor (E-G) scheme to distribute keys for encryption. In terms of
privacy preservation, PPT generates random noise to disturb local model update
parameters. The noise is eliminated ultimately, which ensures the global model
performance compared with other noise-based privacy-preserving methods in FL,
e.g., differential privacy. PPT also adopts Game Theory to resist collusion
attacks. Through extensive analysis, we demonstrate that PPT various security
threats and preserve user privacy. Ingenious experiments demonstrate the
utility and efficiency as well.
