Federated learning is a distributed machine learning paradigm that allows users to learn global models by just providing trained weights to the server. Federated Learning may take advantage of the potential of dispersed data without transferring it to a central server. It has been highlighted as a potential technology that can significantly lessen worries about user data, such as privacy, GDPR rules, and so on. The goal of this project is to create an edge computing infrastructure based on a representative open-source platform and to undertake various studies on federated learning approaches like as:

• Performance evaluation of federated learning algorithm in edge computing environments.

• Research on dynamic resource provisioning and load balancing algorithm to improve federated learning.

• Research on the improved federated algorithm in edge computing environments.