Multi-Brain Federated Learning for Decentralized AI: Collaborative, Privacy-Preserving Models Across Domains
Year: 2025
Type: article
Abstract: Multi-brain Federated Learning (MBFL) introduces an innovative approach to decentralized artificial intelligence, enabling joint model training across various fields while maintaining data privacy. This study clarifies the MBFL concept and explores its potential uses in industries such as healthcare, finance, and defense. It covers the core principles of MBFL such as data decentralization, model aggregation, and privacy-preserving techniques. The benefits of MBFL, including improved model performance and reduction of data silos, are examined along with possible challenges and limitations. A framework for implementing MBFL in different scenarios was provided, and its impact on the future direction of AI development was discussed. The paper concludes by highlighting the transformative potential of MBFL in advancing collaborative AI, while ensuring data security and privacy. Keywords — Multi-brain Federated Learning, Decentralized AI, Privacy-preserving, Collaborative models, Data security, Cross- domain learning, Model aggregation, Federated Learning, Healthcare, Finance, Defense.
DOI: 10.55041/ijsrem19060
Author: Subhasis Kundu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411432652