Current Challenges in Federated Learning: A Review

Federated learning is a privacy-preserving solution for distributed machine learning, allowing participants to solve machine learning problems collaboratively without transmitting their local data to a central server. Instead, they exchange model parameters to achieve the desired outcomes. However, recent scholarly research has revealed several challenges in the traditional federated learning framework. This paper aims to address the issues of communication efficiency, privacy leakage, and client selection algorithms within the federated learning paradigm while exploring potential future research directions.