Federated learning has emerged as a key paradigm in privacy-preserving computing due to its "data usable but not visible" property, enabling users to collaboratively train models without sharing raw data. Motivated by this, federated recommendation systems offer a promising architecture that balances user privacy with recommendation accuracy through distributed collaborative learning. However, existing federated recommendation systems face significant challenges in balancing model performance, communication efficiency, and user privacy. In this paper, we propose FedTLRec (Federated Recommendation with Transformer-based Parameter Aggregation and Collaborative LoRA), which introduces a federat... More >