Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment

Abstract

In recent years, federated learning has been able to provide an effective solution for data privacy protection, so it has been widely used in financial, medical, and other fields. However, traditional federated learning still suffers from single-point server failure, which is a frequent issue from the centralized server for global model aggregation. Additionally, it also lacks an incentive mechanism, which leads to the insufficient contribution of local devices to global model training. In this paper, we propose a blockchain-based decentralized federated learning method, named BD-FL, to solve these problems. BD-FL combines blockchain and edge computing techniques to build a decentralized federated learning system. An incentive mechanism is introduced to motivate local devices to actively participate in federated learning model training. In order to minimize the cost of model training, BD-FL designs a preference-based stable matching algorithm to bind local devices with appropriate edge servers, which can reduce communication overhead. In addition, we propose a reputation-based practical Byzantine fault tolerance (R-PBFT) algorithm to optimize the consensus process of global model training in the blockchain. Experiment results show that BD-FL effectively reduces the model training time by up to 34.9% compared with several baseline federated learning methods. The R-PBFT algorithm can improve the training efficiency of BD-FL by 12.2%.

Towards trustworthy federated learning: a blockchain-based architecture for auditing, traceability, and verification

“…Liu et al 36 These types of frameworks implement the verifiability of federated learning model updates through the blockchain's consensus mechanism, enhancing the trustworthiness of federated learning. These frameworks verify model updates uploaded by federated members before aggregation through a consensus mechanism.…”

A Survey on Blockchain-Based Federated Learning

“…In FL, due to the central aggregation function of the server, once its device is subjected to a single-point attack by adversaries, it poses a significant security risk to the entire learning framework [93][94][95][96]. To enhance the security, trustworthiness, and reliability of the framework, Majeed et al [85] proposed a BFL architecture to improve the security of FL.…”

Federated and Transfer Learning Applications

“…In response to the issues associated with centralization and the absence of incentives in conventional federated learning, Liu et al [8] introduced a decentralized federated learning method for edge computing environments based on blockchain technology, called BD-FL. This approach integrates all edge servers into the blockchain network, where the edge server nodes that acquire bookkeeping rights collectively aggregate the global model.…”

“…With this in mind, the present Special Issue of Applied Sciences on "Federated and Transfer Learning Applications" provides an overview of the latest developments in this field. Twenty-four papers were submitted to this Special Issue, and eleven papers [1][2][3][4][5][6][7][8][9][10][11] were accepted (i.e., a 45.8% acceptance rate). The presented papers explore innovative trends of federated learning approaches that enable technological breakthroughs in highimpact areas such as aggregation algorithms, effective training, cluster analysis, incentive mechanisms, influence study of unreliable participants and security/privacy issues, as well as innovative breakthroughs in transfer learning such as Arabic handwriting recognition, literature-based drug-drug interaction, anomaly detection, and chat-based social engineering attack recognition.…”