A comparative analysis of federated learning techniques on on-demand platforms in supporting modern web browser applications

On-device learning, such as federated learning, is gaining more popularity. It benefits users with faster inference and privacy preservation. However, the heterogeneity of personal devices makes its deployment not easy. With the increasing need for an on-demand learning platform, the web browser has become one of the leading solutions due to its availability and interoperability. Nevertheless, there is still a lack of research on evaluating the behaviour of federated learning on web browser platforms. This includes evaluating their compatibility, convergence in inference results, and performance. Our paper tries to address these concerns. Throughout our experiments, we found that there are still inconsistencies in inference results, compatibility issues, and varied performance among these platforms. Besides experiment analysis on this subject, we also recommend a model-platform-device compatibility report as our contribution.