  Federated learning (FL) has sparked extensive interest in exploiting the
private data on clients' local devices. However, the parameter server setting
of FL not only has high bandwidth requirements, but also poses data privacy
issues and a single point of failure. In this paper, we propose an efficient
and privacy-preserving protocol, dubbed CFL, which is the first fine-grained
global model training for FL in large-scale peer-to-peer (P2P) networks. Unlike
previous FL in P2P networks, CFL aggregates local model update parameters
hierarchically, which improves the communication efficiency facing large
amounts of clients. Also, the aggregation in CFL is performed in a secure
manner by introducing the authenticated encryption scheme, whose key is
established through a random pairwise key scheme enhanced by a proposed
voting-based key revocation mechanism. Rigorous analyses show that CFL
guarantees the privacy and data integrity and authenticity of local model
update parameters under two widespread threat models. More importantly, the
proposed key revocation mechanism can effectively resist hijack attacks,
thereby ensuring the confidentiality of the communication keys. Ingenious
experiments on the Trec06p and Trec07 datasets show that the global model
trained by CFL has good classification accuracy, model generalization, and
rapid convergence rate, and the dropout-robustness of the system is achieved.
Compared to the first global model training protocol for FL in P2P networks,
PPT, CFL improves communication efficiency by 43.25%. Also, CFL outperforms PPT
in terms of computational efficiency.
