PANDAWANPANDAWAN

Blockchain Frontier TechnologyBlockchain Frontier Technology

Federated Learning is a distributed machine learning approach that enables model training without transferring raw data, thereby preserving user privacy. This study explores the integration of blockchain technology to address challenges related to model integrity, including parameter manipulation, model poisoning attacks, and limited trust among participating nodes. Leveraging decentralization, immutability, and transparency, blockchain is used to validate model updates, record contributions, and manage node reputation. The study employs a literature review and technical architecture design for a blockchain-integrated FL system. The results indicate that blockchain implementation enhances the reliability and security of FL training, especially in low-trust environments, with strong relevance for healthcare, finance, and IoT applications.

The study successfully designed an integrative architecture combining blockchain technology with Federated Learning to enhance model integrity.By utilizing blockchain as a transparent and immutable trust layer, the system securely records and verifies each participant nodes contribution.The proposed architecture conceptually demonstrates the potential to enhance integrity, transparency, and trust within Federated Learning systems through blockchain integration.

Future research should focus on developing a prototype of the proposed blockchain-integrated Federated Learning architecture with technical optimizations, including selecting the appropriate blockchain platform and testing at a large scale to empirically evaluate its performance. Furthermore, investigations into the scalability and latency challenges of the system are crucial, exploring solutions like off-chain data storage and optimized consensus mechanisms to ensure efficient operation in real-world deployments. Finally, exploring the integration of differential privacy techniques alongside blockchain could further enhance data security and user privacy, creating a more robust and trustworthy decentralized learning ecosystem that addresses both data integrity and confidentiality concerns. These advancements will pave the way for wider adoption of blockchain-enabled Federated Learning in sensitive domains like healthcare and finance, fostering innovation and collaboration while upholding ethical data handling practices.

  1. #based learning model#based learning model
  2. #according learning model#according learning model
Read online
File size613.19 KB
Pages10
Short Linkhttps://juris.id/p-2py
Lookup LinksGoogle ScholarGoogle Scholar, Semantic ScholarSemantic Scholar, CORE.ac.ukCORE.ac.uk, WorldcatWorldcat, ZenodoZenodo, Research GateResearch Gate, Academia.eduAcademia.edu, OpenAlexOpenAlex, Hollis HarvardHollis Harvard
DMCAReport

Related /

ads-block-test