Federated Learning Approach

Federated learning (FL) is a decentralized machine learning approach enabling multiple parties to collaboratively train a shared model without directly sharing their data, thereby addressing privacy concerns. Current research focuses on improving FL's performance in heterogeneous data settings, employing techniques like influence-oriented aggregation and adaptive feature mixing to enhance accuracy and efficiency across diverse model architectures, including neural networks (e.g., MLPs, LSTMs) and kernel methods. This approach holds significant promise for various applications, from personalized healthcare and smart grids to water conservation and offensive language detection, by enabling the development of robust and accurate models while safeguarding sensitive data.