A Survey on Soft Computing Techniques for Federated Learning- Applications, Challenges and Future Directions

Abstract

Federated Learning is a distributed, privacy-preserving machine learning model that is gaining more attention these days. Federated Learning has a vast number of applications in different fields. While being more popular, it also suffers some drawbacks like high communication costs, privacy concerns, and data management issues. In this survey, we define federated learning systems and analyse the system to ensure a smooth flow and to guide future research with the help of soft computing techniques. We undertake a complete review of aggregating federated learning systems with soft computing techniques. We also investigate the impacts of collaborating various nature-inspired techniques with federated learning to alleviate its flaws. Finally, this paper discusses the possible future developments of integrating federated learning and soft computing techniques.