Federated Learning: Challenges, Methods, and Future Directions

This survey delves into challenges of federated machine learning beyond potential security issues that could affect adoption in industries like financial services. For example, the authors consider how asymmetric data and communications systems might make building networks between heterogenous institutions difficult and increase the costs related to uploading and downloading models or portions of models. These considerations may be especially important in underserved and emerging markets.