Federated Online Adaptation

Federated online adaptation focuses on collaboratively training machine learning models across distributed devices without directly sharing sensitive data, aiming to improve model performance and adaptability in diverse environments. Current research emphasizes efficient algorithms like federated averaging, enhanced by techniques such as adaptive sampling and attention mechanisms to address challenges posed by heterogeneous data and communication constraints. This approach is particularly relevant for applications like medical image analysis and traffic flow prediction, where data privacy is paramount and continuous model improvement is crucial, offering a powerful paradigm for distributed learning.