Application of Federated Learning in Distributed Environments: Experiments and Evaluation
Wei Qiu, Dongxiao Shi, Qiang Li, Yating Zheng, Xiaohui Shen · 2023
Federated Learning stands out as a crucial approach for training machine learning models across decentralized devices, effectively tackling privacy issues and data location constraints. This research delves into the functioning of federated learning within distributed environments and aims to evaluate its effectiveness in ensuring data privacy and improving model performance. Various machine learning models were deployed and scrutinized during the experimentation on the test dataset. Notably, ensemble methods showcased commendable performance while ensuring robust data privacy measures in the context of a randomly generated dataset.