A Federated Learning-Based Patient Monitoring System in Internet of Medical Things

Article Computer Science, Cybernetics

Dynamic Games for Social Model Training Service Market via Federated Learning Approach

Wenqing Cheng et al.

Summary: In recent years, with the profound success of deep learning technologies, new social applications have been emerging and developing, but the issues of user data privacy and security have restricted their broader distribution. Federated learning has become a promising solution to preserve privacy while utilizing deep learning applications.

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2022)

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Article Computer Science, Information Systems

A Sensors Based Deep Learning Model for Unseen Locomotion Mode Identification using Multiple Semantic Matrices

Rahul Mishra et al.

Summary: This paper proposes a sensor-based deep learning model for identifying locomotion modes. The model is capable of identifying new or unseen locomotion modes without training instances, and it builds a classifier by fusing semantic matrices and feature matrices. The performance is evaluated using accuracy and F1 score.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

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Article Computer Science, Cybernetics

Analysis, Modeling, and Representation of COVID-19 Spread: A Case Study on India

Rahul Mishra et al.

Summary: This article discusses various techniques for analyzing, modeling, and representing data related to pandemics such as COVID-19, and proposes corresponding algorithms. It also studies the impact of transmission probability, rate of contact, infectious, and susceptible population on pandemic spread.

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2021)

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Article Computer Science, Information Systems

CDP-UA: Cognitive Data Processing Method Wearable Sensor Data Uncertainty Analysis in the Internet of Things Assisted Smart Medical Healthcare Systems

Gunasekaran Manogaran et al.

Summary: The Internet of Medical Things platform connects sensors, users, and clinical centers to provide solutions for disease diagnosis and health monitoring. Cognitive data processing is introduced to address uncertainties in data management, improving efficiency in aggregation and dissemination processes. This method helps classify overloaded and latency-centric instances to enhance data delivery in medical analysis centers.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

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Proceedings Paper Computer Science, Theory & Methods

A Network Resource Aware Federated Learning Approach using Knowledge Distillation

Rahul Mishra et al.

Summary: This poster proposes a federated learning approach that utilizes knowledge distillation to train a machine learning model, creating different groups based on bandwidth restrictions between clients and server. By compressing the model using knowledge distillation, the approach reduces bandwidth requirements, generates a more robust model, and protects data privacy.

IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM WKSHPS 2021) (2021)

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Article Automation & Control Systems