Abstract:

Federated learning is a new machine learning paradigm that allows multiple participants to collaboratively train a shared machine learning model in a private and secure way without sharing raw data. Federated learning has wide application value because it can solve the problem of data island. However, in traditional federated learning, using a single central server aggregation model can lead to a single point of failure problem. In order to overcome the possible single point of failure problem in traditional federated learning, this study proposes a blockchain-based distributed federation learning (DFL), which takes advantage of the characteristics of blockchain to delegate the task of storing the model to nodes in the blockchain network. An asynchronous aggregation strategy is proposed, which enables participants to join federated learning at any time reducing the waiting time of participants. To overcome the blockchain storage limitation, a model chunking strategy is designed to chunk the large-scale model to fit the blockchain storage requirements. The proposed DFL is evaluated by training multiple machine learning models on multiple data sets, and the experimental results show that DFL achieves better performance than traditional methods while overcoming single point of failure.

Key words: federated learning, privacy secure, data silos, blockchain, single point of failure, distributed, asynchronous aggregation, model chunkin