Work

Sovereign Ai on the Edge: a Decentralized Framework for Federated Learning Using Multimodal Foundation Models in Low-resource Indian Environments Towards Ethical and Scalable Machine Learning Architectures for Language, Agriculture, and Healthcare in Emerging Economies

Year: 2025

Type: article

Abstract: The ethical and scalable AI infrastructure is a pressing need in emerging economies. This paper proposes a decentralized framework combining Federated Learning (FL) with Multimodal Foundation Models (MFMs) for use in low-resource Indian contexts. Focusing on agriculture, healthcare, and language, we aim to build Sovereign AI systems that preserve data privacy while enhancing accessibility and adaptability. Our approach eliminates central dependency by leveraging edge computing, enabling real-time learning on local devices. This design ensures autonomy, inclusiveness, and fairness—factors critical to India's rural AI landscape.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15646907

Author: Researcher

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Reduced inequalities

Open Access status: green

OpenAlex ID: W6949224399

FWCI:

Cites:

Cited by:

Related to: