#federated-learning

#federated-learning

Federated learning is a distributed (decentralized) ML technique that enables training models by moving the training process to where the data is, instead of collecting and moving the data to the central server.

In our experiments, we attempted the following standard datasets in the machine learning literature to assess the effectiveness of DP-based Fair Learning.

The study shows that PPFL methods exhibit superior computational efficiency over DPFL methods due to their reliance on predefined physical models, avoiding training processes.