IISc’s CDPG Enables Responsible AI Hackathon on National Health Data with NHA

– CDPG’s Trusted AI Data Platform Powers AB PM-JAY Auto-Adjudication Hackathon Showcase 2026 Bengaluru, May 11: The National Health Authority (NHA), under the Ministry of Health and Family Welfare (MoHFW), in collaboration with the IndiaAI Mission and the Indian Institute of Science (IISc), Bengaluru, successfully concluded the AB PM-JAY Auto-Adjudication Hackathon Showcase 2026 on Saturday, May 9. The hackathon was technically …

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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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