Solid Waste Pickup & Route Optimization
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CDPG
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February 21, 2025
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IUDX
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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 often …
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Smart e-Governance
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CDPG
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February 10, 2025
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E-Governance
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(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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Agriculture Data Exchange
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CDPG
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February 10, 2025
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ADeX
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(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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Technical Overview and Architecture of IUDX Platform
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CDPG
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February 10, 2025
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IUDX
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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 often …
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Private Data Quality Assessment for Smart Cities
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CDPG
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February 10, 2025
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CDPG
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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 often be substantial heterogeneity (e.g., …
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Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
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CDPG
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February 7, 2025
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Data Privacy
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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 often be substantial heterogeneity (e.g., …
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Privacy-Preserving Data Quality Assessment for Time-Series IoT Sensors
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CDPG
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February 7, 2025
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Data Privacy
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Privacy-Preserving Data Quality Assessment for Time-Series IoT Sensors Authors: N. Chakraborty, A. Sharma, J. Dutta. H. D. Kumar This paper proposes a novel framework for automated, objective, and privacy-preserving data quality assessment of time-series data from IoT sensors deployed in smart cities. We leverage custom, autonomously computable metrics that parameterise the temporal performance and adherence to a declarative schema document …
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Integrated Geospatial Data-Sharing Interface: Building Digital Public Infrastructure for Geospatial Data
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CDPG
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February 6, 2025
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Whitepaper
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Automatable Data Quality Dimensions for Data Exchange: Formulation and Application
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CDPG
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February 4, 2025
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Data Policy
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Automatable Data Quality Dimensions for Data Exchange: Formulation and Application Authors: Debarun Sengupta, Anjula Gurtoo, Minnu Malieckal, Jyotirmoy Dutta Large amounts of data get generated and applied in decision making to improve outcomes. However, quality of the data remains an issue as data gets generated from varied sources, in unspecified formats, and variables vary across different types of data. Identifying …
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Ethics and Fair Use Framework for Privacy Preserving Data Sharing
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CDPG
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February 4, 2025
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Data Policy
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Ethics and Fair Use Framework for Privacy Preserving Data Sharing Authors: Bita Afsharinia, Anjula Gurtoo, Jyotirmoy Dutta, Minnu Malieckal The study aims to critically evaluate current privacy-preserving technologies and ethical frameworks in data sharing, identifying gaps and proposing a comprehensive, integrated ethical framework. Existing frameworks often fall short in integrating these two aspects effectively, particularly in the context of emerging …
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