How You Can Use Federated Learning for Security & Privacy

As another example of future FL trends – enabling parallel training of deep learning models on distributed data sets while preserving data privacy is complex and challenging. One group of researchers has developed a federated learning framework FEDF for privacy-preservation coupled with parallel training. The framework allows a model to be learned on multiple geographically-distributed training data sets (which may belong to different owners) while not revealing any information of each data set as well as the intermediate results.