Solid Waste Pickup & Route Optimization

  • February 21, 2025
  • IUDX
  • 0 Comments

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 …

Continue Reading

Smart e-Governance

(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 …

Continue Reading

Agriculture Data Exchange

  • February 10, 2025
  • ADeX
  • 0 Comments

(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 …

Continue Reading

Technical Overview and Architecture of IUDX Platform

  • February 10, 2025
  • IUDX
  • 0 Comments

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 …

Continue Reading

Private Data Quality Assessment for Smart Cities

  • February 10, 2025
  • CDPG
  • 0 Comments

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., …

Continue Reading

Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning

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., …

Continue Reading

Privacy-Preserving Data Quality Assessment for Time-Series IoT Sensors

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 …

Continue Reading

Automatable Data Quality Dimensions for Data Exchange: Formulation and Application

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 …

Continue Reading

Ethics and Fair Use Framework for Privacy Preserving Data Sharing

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 …

Continue Reading