The challenges of handling decentralised data lead to the demand for research on secure gathering,
efficient processing, and analysing of the data. In decentralised systems, each node (device) can make
independent decisions, reducing the complexity and challenges of dealing with extensive data. Privacy
has become a significant concern for our society due to the rise in the number of Edge/IoT devices,
lack of presence of a centralised system etc. To solve this conundrum, federated learning was proposed.
Federated learning works on the sharing of parameter values rather than the data. Worldwide
10.2 Billion non-IoT and 19.8 billion IoT devices will be active in 2023. These devices lack security
when it comes to using traditional machine learning. However, federated learning models solve this
problem using techniques such as Secure Aggregation and Differential Privacy, which provide security
for the devices and efficient communication between them. The challenges arise from heterogeneous
devices, leading to the client selection problem, unbalanced data, and many more problems. Our work
focuses on providing federated learning strategies to solve problems in non-IoT and IoT-based devices.