Blockchain-enabled Federated Learning:Models,Methods and Applications
In recent years,human society has been witnessed to evolve fast to the era of big data,rendering the data security and privacy protection a key issue for the development of digital economies.Federated learning,as a novel pattern for distributed machine learning,is aimed to train a centralized model from decentralized datasets while protecting user privacy,and is now intensively studied in literature.However,a variety of technical chal-lenges,e.g.,centralized architecture,incentive mechanism design,and system-wide security issues,are still awaiting further research efforts.In this respect,blockchain proves to be an elegant solution for federated learning to over-come these issues,and thus has been applied in federated learning in many scenarios with success.In this paper,we proposed the conceptual model for blockchain-enabled federated learning(BeFL)based on a comprehensive review of related literatures,and discussed the key techniques,research issues,as well as the state-of-the-art research pro-gresses.We also investigated potential application scenarios,several key issues to be addressed and the future trends.Our work is aimed at offering useful reference and guidance for establishing a new infrastructure for decent-ralized,secured and trusted data ecosystem,and also promoting the development of digital economy industries.
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