Strategic India-Japan Partnership to Accelerate AI-Readiness and Construction/Urban Data Exchange for Smart Cities

Japan’s ONESTRUCTION, IISc-backed CDPG/DataKaveri sign MoU to collaborate on integration of construction data into AI-ready urban data exchanges for cities. ONESTRUCTION are experts in open standard construction data and DataKaveri already supports 55 smart cities with urban planning. BENGALURU, India — April 23, 2026: ONESTRUCTION Inc. (ONESTRUCTION株式会社), a Tottori-based construction technology company and DataKaveri Systems, the commercial entity of IISc Bengaluru’s Centre of Data for Public …

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CDPG-Associated Initiatives ‘TGDeX’ and ‘GDI’ Honoured at The Economic Times GovTech Awards 2026

New Delhi/Bengaluru, 19 March, 2026: The Centre of Data for Public Good, Indian Institute of Science, Bangalore is pleased to announce that two initiatives it has developed and supported – Telangana Data Exchange (TGDeX) and the Integrated Geospatial Data-sharing Interface (GDI) – have been recognized at the prestigious The Economic Times GovTech Awards 2026. Developed through collaborative efforts with government …

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Harnessing The Power Of Data For Better Tomorrow

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

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Intelligent Universal Data Exchange for Agriculture Innovation

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

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Empowering Healthcare AI with Accessible Data

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

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IUDX Mobility Unlocking movement Unleashing Growth

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

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Intelligent Universal Data ExchangeIUDX-Novo 1.0

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

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Advanced Protection for AgriAI

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

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Comprehensive Data Framework for State Government AI

(ADeX) Architecture of IUDX Platform Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can …

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Maharashtra Agriculture Data Exchange

Website Abstract: MahaAgX is a secure, interoperable and consent-based agricultural data exchange platform that enables researchers, innovators and policymakers to access diverse agricultural datasets for smarter governance, accelerating innovation, and building Al-powered solutions.

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