BAFL: An Efficient Blockchain-Based Asynchronous Federated Learning Framework

With the widespread of 5G networks, the application of Federated Learning (FL) in Internet of Things (IoT) has become a trend. However, the trust problem caused by the centralized aggregation server, and the inefficiency problem caused by the low-performance devices, are still key challenges. Several studies involving asynchronous FL have been conducted to accelerate the training process, but they usually have a decreased model performance. In this paper, a blockchain-based asynchronous federated learning framework with a dynamic scaling factor is proposed. By adopting the blockchain, the trust problem among devices can be addressed. Meanwhile, the novel dynamic scaling factor is proposed to help improve the FL efficiency and accuracy. Extensive experiments are conducted on heterogeneous devices and the results show that the proposed framework mitigates the impact of low-performance devices while being as efficient as traditional FL with the extra benefit of alleviating the trust problem among IoT devices.

I. Introduction

The growing importance of distributed systems gives rise to Federated Learning (FL) [1], which is proposed as a distributed machine learning framework and protects the privacy of the user training data. With the fast proliferation of 5G technology, it is an emerging trend to train FL models on heterogeneous devices, such as IoT and edge devices [2]. However, in classic FL frameworks, a centralized authoritative server is required and users must unconditionally trust local models uploaded by other unverified devices. Besides, the synchronous global aggregation drags down the convergence speed, which poses further threats to the application of FL.