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Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized.
Federated learning is a Distributed Machine Learning model that has been used in many applications today. Most edge devices can execute models with local dataset since their computation power is unutilized.
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Autorenporträt
Saravanan Krishnan is working as Associate Professor at the Department of Computer Science & Engineering, College of Engineering, Guindy, Anna University, Tirunelveli, India. He has published papers in 14 international conferences and 30 reputed journals. He has also written 16 book chapters and nine books with reputed publishers. He is an active researcher and academician. Also, he is reviewer for many reputed journals published by Elsevier, IEEE etc. A. Jose Anand is working as Professor at the Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, India. He has one year of industrial experience and twenty-four years of teaching experience. He has presented several papers at conferences. He has published several papers in reputed journals. He has also published books for polytechnic & engineering subjects. He is a Member of CSI, IEI, IET, IETE, ISTE, INS, QCFI and EWB. His current research interest is in Wireless Sensor Networks, Embedded Systems, IoT, Machine Learning and Image Processing, etc. R. Srinivasan is working as Professor at the Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India having vast teaching experience. He received a Ph.D. in Computer Science and Engineering from Vel Tech University. His research interest spans across Computer Networking, Wireless Sensor Networks and Internet of Things (IoT). Much of his work has been on improvising the understanding, design and the performance of networked computer systems and performance evaluation. He is a recognised supervisor at Vel Tech University guiding 8 research scholars. He has published over 25 papers in reputed journals and conferences. He had delivered technical sessions to various reputed institutes. He has been a reviewer member for many conferences and has served as technical committee member. He is also a member in many professional societies and a member in IEEE. He has published several reputed articles. He is presently Editor in Chief for Wireless Networks, Peer-to-Peer Networking and Applications- Springer Series. R. Kavitha received a master's in software engineering from College of Engineering, Anna University, India and Ph. D in Computer Science and Engineering from Vel Tech, Chennai, India. Her research areas are Machine Learning, Image Processing and Software Engineering. She worked as Professor at Vel Tech, Chennai with 15 years of teaching experience. She had guided projects of many UG and PG students. She is a recognised supervisor at Vel Tech University guiding 8 research scholars. She has published over 35 papers in reputed journals. She is an active member of IEEE and IEEE WIE and has been a part of events in association with professional societies. She had delivered technical sessions to various reputed institutes. She has been a reviewer member for many conferences and has served as technical committee member. S. Suresh was a Professor of Cloud Big Data and Analytics, Faculty of Computer Science and Engineering at P.A. College of Engineering and Technology, India. He undertook extensive research on Big Data & Analytics, Internet of Things and Machine Learning. He wrote more than 30 scientific papers some of which have been published in well-known journals from Elsevier, Springer, etc. and presented at important conferences. In his lifetime, he had received various best paper and best speaker awards. Suresh authored 6 books and numerous book chapters. He fetched research and events grants from various Indian agencies. His research is summarized at Google Scholar Citation. He also regularly tutors, advises and provides consulting support to regional firms with respect to their Cloud Big Data Analytics, IoT, Machine Learning and Mobile Application Development.
Inhaltsangabe
Introduction to Federated Learning: Methods, and Classifications. Go Local, Go Global and Go Fusion - How to pick data from various contexts. Federated Learning Architectures, Opportunities, and Applications. Secure and Private Federated Learning through Encrypted Parameter Aggregation. Navigating Privacy Concerns in Federated Learning: A GDPR-Focused Analysis. A Federated Learning Approach for Resource-Constrained IoT Security Monitoring. Efficient Federated Learning Techniques for Data Loss Prevention in Cloud Environment. Maximizing Fog Computing Efficiency with Federated Multi-Agent Deep Reinforcement Learning. Future of Medical Research with a data-driven Federated Learning Approach. Collaborative Federated Learning in Healthcare Systems. Federated Learning for Efficient Cardiac Disease Prediction based on Hyper Spectral Feature Selection using Deep Spectral Convolution Neural Network. A Federated Learning based Alzheimer's Disease Prediction. Detecting Device Sensors of Luxury Hotel Using Blockchain Based Federated Learning to Increase Customer Satisfaction. Navigating the Complexity of Macro-Tasks: Federated Learning as a Catalyst for Effective Crowd Coordination. Stock Market Prediction via Twitter Sentiment Analysis using BERT: A Federated Learning Approach.
Introduction to Federated Learning: Methods, and Classifications. Go Local, Go Global and Go Fusion - How to pick data from various contexts. Federated Learning Architectures, Opportunities, and Applications. Secure and Private Federated Learning through Encrypted Parameter Aggregation. Navigating Privacy Concerns in Federated Learning: A GDPR-Focused Analysis. A Federated Learning Approach for Resource-Constrained IoT Security Monitoring. Efficient Federated Learning Techniques for Data Loss Prevention in Cloud Environment. Maximizing Fog Computing Efficiency with Federated Multi-Agent Deep Reinforcement Learning. Future of Medical Research with a data-driven Federated Learning Approach. Collaborative Federated Learning in Healthcare Systems. Federated Learning for Efficient Cardiac Disease Prediction based on Hyper Spectral Feature Selection using Deep Spectral Convolution Neural Network. A Federated Learning based Alzheimer's Disease Prediction. Detecting Device Sensors of Luxury Hotel Using Blockchain Based Federated Learning to Increase Customer Satisfaction. Navigating the Complexity of Macro-Tasks: Federated Learning as a Catalyst for Effective Crowd Coordination. Stock Market Prediction via Twitter Sentiment Analysis using BERT: A Federated Learning Approach.
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