Enhancing Trust and Privacy in Distributed Networks: A Comprehensive
  Survey on Blockchain-based Federated Learning
Ji Liu Chunlu Chen Yu Li Lin Sun Yulun Song Jingbo Zhou Bo Jing Dejing Dou
While centralized servers pose a risk of being a single point of failure,
decentralized approaches like blockchain offer a compelling solution by
implementing a consensus mechanism among multiple entities. Merging distributed
computing with cryptographic techniques, decentralized technologies introduce a
novel computing paradigm. Blockchain ensures secure, transparent, and
tamper-proof data management by validating and recording transactions via
consensus across network nodes. Federated Learning (FL), as a distributed
machine learning framework, enables participants to collaboratively train
models while safeguarding data privacy by avoiding direct raw data exchange.
Despite the growing interest in decentralized methods, their application in FL
remains underexplored. This paper presents a thorough investigation into
Blockchain-based FL (BCFL), spotlighting the synergy between blockchain's
security features and FL's privacy-preserving model training capabilities.
First, we present the taxonomy of BCFL from three aspects, including
decentralized, separate networks, and reputation-based architectures. Then, we
summarize the general architecture of BCFL systems, providing a comprehensive
perspective on FL architectures informed by blockchain. Afterward, we analyze
the application of BCFL in healthcare, IoT, and other privacy-sensitive areas.
Finally, we identify future research directions of BCFL.