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

PPT: A Privacy-Preserving Global Model Training Protocol for Federated Learning in P2P Networks