An introduction to federated learning

The Department of Electronics and Communication Engineering is hosting a seminar on September 16, 2022, at 4 pm as part of the Departmental Distinguished Lecture Series. Dr Anurag Singh, Dean of Research and Consultancy, NIT Delhi, will deliver a talk on the topic “Federated Learning”.

Abstract of the talk

The most crucial resource for any business, individual, or person in the world is data. Everyone, whether an individual or an institution, wants to prevent a data breach. High-quality data must be subjected to machine learning algorithms. The model is trained using traditional machine learning techniques, which save data to one server. There is a chance that this method will expose personal information. A machine learning technique called Federated Learning (FL) enables machine learning models to train on various datasets located on various sites without sharing data. Without putting training data in a centralised location, it enables the development of a common global model. Additionally, it permits personal information to stay in local places, lowering the risk.

A new area of machine learning called federated learning already offers greater advantages than conventional machine learning techniques.

Data Security: Training data is kept locally on the devices, negating the need for a data pool.

Data variety: Heterogeneous data since it incorporates information from various users.

Real-time continuous learning: Client data is used to enhance models continuously.

Federated Learning is applied in the field of IoT, Healthcare, smartphones, Advertising, Autonomous Vehicles etc.

Speaker’s Profile

Dr Anurag Singh is currently working as the Associate Professor and Dean of Research and Consultancy at NIT Delhi. He received his PhD from the Indian Institute of Technology Kanpur. His research areas are Network Theory, Dynamics on/of Networks, Opinion Dynamics, Epidemic Modeling, Intelligent transportation system etc. Dr Anurag Singh has teaching experience at both undergraduate and graduate levels. He has taught courses ranging from the introductory level to specialized courses in Computer Science and Engineering, and Mathematics. Currently, he is mentoring students at graduate and undergraduate levels at the National Institute of Technology Delhi, India. In addition, he has also been supervising PhD students. He has around 70 publications featured across various leading journals and three DST-funded projects to his credit.

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