I just started studying federated learning and want to apply it to a certain dataset, and there are some questions that have risen up.
My data is containing records of 3 categories, each of which is having 3 departments. I am planning to have 3 different federated learning models for each category and treat the three department of this category as the distributed clients.
Is this possible? or building federated learning models requires having thousands of clients?
Thanks
Difficult to say by what you have provided in your question. Usually, when building a federated learning system, you are extending your centralized approach to one with data split/partitioned between segregated clients. Again, depending on the type of data you have, the type of task you are trying to solve and also the amount of data required to solve the task in a centralized approach, these factors along with other ones will depend how many clients you can use and how much data is required at each client. Additionally, the aggregation method that you wish to use combine the parameters from different clients will affect this. I suggest experimenting with different client numbers and partitioning methods and seeing what suits your needs.