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Showing posts from July, 2022

Combined Federated Data Services with Blossom and Flower

When it comes to Federated Learning frameworks we typically find two leading open source projects - Apache Wayang [2] (maintained by  databloom ) and Flower [3] (maintained by  Adap ). And at the first view both frameworks seem to do the same. But, as usual, the 2nd view tells another story. How does Flower differ from Wayang? Flower is a federated learning system, written in Python and supports a large number of training and AI frameworks. The beauty of Flower is the strategy concept [4]; the data scientist can define which and how a dedicated framework is used. Flower delivers the model to the desired framework and watches the execution, gets the calculations back and starts the next cycle. That makes Federated Learning in Python easy, but also limits the use at the same time to platforms supported by Python.  Flower has, as far as I could see, no data query optimizer; an optimizer understands the code and splits the model into smaller pieces to use multiple frameworks at the same ti