How to build federated learning model of unbalanced and small dataset

Question

I am working to build a federated learning model using TFF and I have some questions:

1. I am preparing the dataset, I have separate files of data, with same features and different samples. I would consider each of these files as a single client. How can I maintain this in TFF?

2. The data is not balanced, meaning, the size of data varies in each file. Is this affecting the modeling process?

3. The size of the data is a bit small, one file (client) is having 300 records and another is 1500 records, is it suitable to build a federated learning model?

Thanks in advance