Theoretical Analysis, Practical Defects and Optimization Paths of Coupling Structures Between Blockchain and Federated Learning

Abstract

Federated learning, featuring "data available but not visible", enables cross-entity collaborative modeling while protecting data privacy. However, its traditional centralized architecture suffers from inherent drawbacks, including single-point failure, lack of trust, and insufficient incentive mechanisms. Blockchain technology, with its decentralization, immutability, and smart contract automation features, provides a targeted technical solution to address these issues. Recent studies have proposed three core coupling structures for the integration of blockchain and federated learning, namely the fully decoupled type, the overlapping type, and the overlapping transformable type, laying a solid framework for the in-depth integration of the two technologies. Taking these three architectures as the research object, this paper systematically analyzes their core connotations and theoretical adaptability by drawing on practical literatures from multiple fields, and focuses on exploring their prominent problems and underlying technical causes in practical deployment. On this basis, it puts forward operable optimization schemes from three dimensions: structure-specific optimization, common technical breakthroughs, and scenario-based adaptation, clarifies key directions such as lightweight integration and dynamic mechanism design, and provides technical references for the practical deployment and performance improvement of BlockFed architectures.