FedCF Method

Federated Collaborative Filtering (FedCF) aims to build recommendation systems that preserve user privacy by training models on decentralized data. Current research focuses on improving communication efficiency through techniques like frequency-space transformations and addressing challenges posed by non-independent and identically distributed (non-IID) data using adaptive learning rates and personalized model architectures such as variational autoencoders. These advancements enhance the accuracy and scalability of federated learning for recommendation systems, impacting both the development of privacy-preserving machine learning and the personalization of user experiences.