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

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.