A Comprehensive Analysis of Various Federated Learning Techniques

A method for data privacy maintenance while training machine learning models across decentralized devices is Federated Learning (FL). It comes under the category of decentralized field of machine learning. The decentralized approach faces various challenges, one of them is aggregating model updates which come from diverse and potentially unreliable sources. This study provides a comparison review of various algorithms for privacy preservation, and maintaining security and also talks about the various federated averaging techniques. These techniques are essential for an efficient model aggregation. This research paper also provides an analysis of strengths, weaknesses, and various application areas of federated learning. This paper has included the maximum techniques available for federated learning by comparing their datasets and various other parameters. This paper also provides insight into the related future work that is proposed in various research articles.