My current research focuses on distributed machine learning and building efficient algorithms for IoT devices.

Federated Learning

Federated learning is an emerging distributed machine learning technique which does not require the transmission of data to a central server to build a global model. Instead, individual devices build their own models, and the model parameters are transmitted. The server constructsa global model using these parameters, which is then re-transmitted back to the devices. The major bottleneck of this approach is the communication overhead as all the devices need to transmit their model parameters at regular intervals. To overcome this challenge, we have proposed a new approach known as Fusion learning, where the clients compute the distribution parameters of each feature of the dataset along with the model parameters and transmit them to the server.