  Specializing Directed Acyclic Graph Federated Learning(SDAGFL) is a new
federated learning framework which updates model from the devices with similar
data distribution through Directed Acyclic Graph Distributed Ledger Technology
(DAG-DLT). SDAGFL has the advantage of personalization, resisting single point
of failure and poisoning attack in fully decentralized federated learning.
Because of these advantages, the SDAGFL is suitable for the federated learning
in IoT scenario where the device is usually battery-powered. To promote the
application of SDAGFL in IoT, we propose an energy optimized SDAGFL based
event-triggered communication mechanism, called ESDAGFL. In ESDAGFL, the new
model is broadcasted only when it is significantly changed. We evaluate the
ESDAGFL on a clustered synthetically FEMNIST dataset and a dataset from texts
by Shakespeare and Goethe's works. The experiment results show that our
approach can reduce energy consumption by 33\% compared with SDAGFL, and
realize the same balance between training accuracy and specialization as
SDAGFL.
