From Distributed Consensus to Privacy-Preserving Federated Learning: Technical Advances and Open Challenges in Blockchain Systems

Abstract

Blockchain has evolved from its initial use in cryptocurrency into a foundational infrastructure that enables secure, transparent, and decentralized applications across diverse domains such as finance, healthcare, supply chain management, and the Internet of Things (IoT). By integrating distributed ledgers, consensus protocols, and cryptographic primitives, blockchain eliminates the need for centralized intermediaries and provides tamper-resistant data sharing among untrusted participants. However, the technology faces persistent challenges, including scalability limitations, high energy consumption, privacy leakage, and a lack of interoperability among heterogeneous platforms. This survey presents a comprehensive review of blockchain technology, systematically examining the evolution of consensus mechanisms, scalability optimization strategies, and privacy-preserving frameworks. In addition, it highlights key application domains, industrial adoption trends, and the integration of blockchain with emerging paradigms such as artificial intelligence, federated learning, and edge computing. The paper further discusses open challenges related to performance, security, compliance, and socio-technical adoption, offering insights into the future research directions needed to enable secure, scalable, and sustainable decentralized systems.