论文标题
基于事件触发的沟通的能量优化专门的DAG联合学习
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication
论文作者
论文摘要
专门针对有向的无环图联合学习（SDAGFL）是一个新的联合学习框架，它通过导向的无循环图分布式分类帐技术（DAG-DLT）从设备上更新模型。 SDAGFL具有个性化的优势，可抵抗完全分散的联合学习中的单点失败和中毒攻击。由于这些优点，SDAGFL适用于在设备通常由电池供电的物联网方案中的联合学习。为了促进SDAGFL在IoT中的应用，我们提出了一种基于ESDAGFL的基于事件触发的通信机制的能量优化的能源。在ESDAGFL中，仅当新模型发生显着更改时才会广播。我们在莎士比亚和歌德的作品的群集合成女性数据集中评估了eSDAGFL的合成女性数据集和数据集。实验结果表明，与SDAGFL相比，我们的方法可以将能源消耗降低33 \％，并在训练准确性和专业化之间达到与SDAGFL相同的平衡。
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.
