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

by   Qian Chen, et al.

The concept of Federated Learning has emerged as a convergence of distributed machine learning, information, and communication technology. It is vital to the development of distributed machine learning, which is expected to be fully decentralized, robust, communication efficient, and secure. However, the federated learning settings with a central server can't meet requirements in fully decentralized networks. In this paper, we propose a fully decentralized, efficient, 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. PPT also adopts Neighborhood Broadcast, Supervision and Report, and Termination as complementary mechanisms to enhance security and robustness. Through extensive analysis, we demonstrate that PPT resists various security threats and preserve user privacy. Ingenious experiments demonstrate the utility and efficiency as well.


page 1

page 4

page 10


Communication-Efficient Cluster Federated Learning in Large-scale Peer-to-Peer Networks

A traditional federated learning (FL) allows clients to collaboratively ...

Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph

Establishing how a set of learners can provide privacy-preserving federa...

Decentralized Federated Learning with Unreliable Communications

Decentralized federated learning, inherited from decentralized learning,...

Peer-to-peer Federated Learning on Graphs

We consider the problem of training a machine learning model over a netw...

Secure Byzantine-Robust Machine Learning

Increasingly machine learning systems are being deployed to edge servers...

Homogeneous Learning: Self-Attention Decentralized Deep Learning

Federated learning (FL) has been facilitating privacy-preserving deep le...

Please sign up or login with your details

Forgot password? Click here to reset