Genomics data is complex, high dimensional and confidential. Analysis of such type of data can lead to better understanding of diseases such as cancer. Single cell RNA sequencing is a recent advancement in technology for analyzing cancer at the single cell level. Deep learning techniques have been effectively used by researchers to analyze genomics data. These techniques include feed forward neural networks (FFNN), convolutional neural network (CNN) and long short term memory (LSTM) networks. Federated learning is a framework that involves sharing and aggregation of the machine learning model instead of the data. The project investigates the performance of deep learning techniques for cancer genomics data when implemented using federated learning framework. The two cancer datasets from the Gene Expression Omnibus (GEO) database are identified for the project that contains cell type information based on the gene expressions data. The three deep learning techniques are applied to solve a classification problem. The performance of the models is measured in terms of the f1-score due to class imbalance as these datasets are from tumor sites; therefore the majority class is that of the tumor cell type. Each of the three deep learning models is implemented using the centralised learning framework as well as the federated learning framework. The results demonstrate that the performance of the deep learning models using federated learning for gene expression data is slightly better as compared to that using the centralised learning framework. This supports the fact for further investigation into building deep learning models for heterogeneous genomics data using federated learning to better understand complex diseases such as cancer.