Federated Approach

Federated learning is a distributed machine learning approach aiming to collaboratively train models across multiple decentralized datasets without directly sharing the data itself. Current research focuses on adapting various model architectures, including neural networks (like UNets and transformers), to federated settings, often incorporating techniques like knowledge distillation or low-rank decomposition to reduce communication overhead and improve efficiency. This approach is proving valuable in diverse applications, from medical image analysis and personalized recommendations to environmental monitoring and hate speech detection, by enabling the development of powerful models while preserving data privacy and addressing data heterogeneity across sources.