FBChain: A Blockchain-based Federated Learning Model with Efficiency and
  Secure Communication
Yang Li Chunhe Xia Wei Liu Weidong Zhou Chen Chen Tianbo Wang
Privacy and security in the parameter transmission process of federated
learning are currently among the most prominent concerns. However, there are
two thorny problems caused by unprotected communication methods:
"parameter-leakage" and "inefficient-communication". This article proposes
Blockchain-based Federated Learning (FBChain) model for federated learning
parameter communication to overcome the above two problems. First, we utilize
the immutability of blockchain to store the global model and hash value of
local model parameters in case of tampering during the communication process,
protect data privacy by encrypting parameters, and verify data consistency by
comparing the hash values of local parameters, thus addressing the
"parameter-leakage" problem. Second, the Proof of Weighted Link Speed (PoWLS)
consensus algorithm comprehensively selects nodes with the higher weighted link
speed to aggregate global model and package blocks, thereby solving the
"inefficient-communication" problem. Experimental results demonstrate the
effectiveness of our proposed FBChain model and its ability to improve model
communication efficiency in federated learning.