  Many researchers have proposed replacing the aggregation server in federated
learning with a blockchain system to improve privacy, robustness, and
scalability. In this approach, clients would upload their updated models to the
blockchain ledger and use a smart contract to perform model averaging. However,
the significant delay and limited computational capabilities of blockchain
systems make it inefficient to support machine learning applications on the
blockchain.
  In this paper, we propose a new public blockchain architecture called DFL,
which is specially optimized for distributed federated machine learning. Our
architecture inherits the merits of traditional blockchain systems while
achieving low latency and low resource consumption by waiving global consensus.
To evaluate the performance and robustness of our architecture, we implemented
a prototype and tested it on a physical four-node network, and also developed a
simulator to simulate larger networks and more complex situations. Our
experiments show that the DFL architecture can reach over 90\% accuracy for
non-I.I.D. datasets, even in the presence of model poisoning attacks, while
ensuring that the blockchain part consumes less than 5\% of hardware resources.
