Centralized federated learning model based on model accuracy
SONG Yubo1,2, ZHU Jingkai1,2, ZHAO Lingqi1,2, HU Aiqun2,3
1. Jiangsu Key Laboratory of Computer Networking Technology, School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China; 2. Purple Mountain Laboratories, Nanjing 211189, China; 3. State Key Laboratory of Mobile Communications, School of Information Science and Engineering, Southeast University, Nanjing 211189, China
Abstract：Existing federated learning models have problems due to malicious central servers and malicious participants publishing false data that poisons the model. A decentralized federated learning model was developed to address these problems by moving the aggregation work from the central server to the participants' computers. Each participant uses the aggregation algorithm to write the trained model parameters into the transaction and generates blocks that are then published to the blockchain network. A Byzantine fault-tolerant consensus algorithm based on model accuracy is used to build a consensus group and the nodes are dynamically joined by establishing a node information table. The results show that under the same conditions, compared with the traditional Byzantine fault-tolerant consensus algorithm, the throughput of the high-performance Byzantine fault-tolerant consensus algorithm based on model accuracy is increased by 60%, and the average system delay is reduced from 6 s to 1 s.
宋宇波, 朱靖恺, 赵灵奇, 胡爱群. 基于模型准确率的链上去中心化联邦学习模型[J]. 清华大学学报（自然科学版）, 2022, 62(5): 832-841.
SONG Yubo, ZHU Jingkai, ZHAO Lingqi, HU Aiqun. Centralized federated learning model based on model accuracy. Journal of Tsinghua University(Science and Technology), 2022, 62(5): 832-841.
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2022, Vol. 62 
