# An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication

arXiv: https://arxiv.org/abs/2209.12531 · HTML: https://mlsanity.com/paper/2209.12531/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Asynchronous Federated Learning on Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2109.04269/) (2021)
- [Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains](https://mlsanity.com/paper/1801.10228/) (2018)
- [Distributed Deep Learning with Event-Triggered Communication](https://mlsanity.com/paper/1909.05020/) (2019)
- [Implicit Model Specialization through DAG-based Decentralized Federated Learning](https://mlsanity.com/paper/2111.01257/) (2021)
- [EventGraD: Event-Triggered Communication in Parallel Machine Learning](https://mlsanity.com/paper/2103.07454/) (2021)
- [AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent](https://mlsanity.com/paper/2112.13935/) (2021)

## Abstract (arXiv)

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.