Robust Softmax Aggregation on Blockchain based Federated Learning with Convergence Guarantee

【Author】 Wu, Huiyu; Klabjan, Diego

【Source】2024 IEEE INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS, COINS 2024

【影响因子】

【Abstract】Blockchain based federated learning is a distributed learning scheme that allows model training without participants sharing their local data sets, where the blockchain components eliminate the need for a trusted central server. In this paper we propose a softmax aggregation blockchain based federated learning framework. First, we propose a new blockchain based federated learning architecture that utilizes the well-tested proof-of-stake consensus mechanism on an existing blockchain network to select validators and miners to aggregate the participants' updates and compute the blocks. Second, to ensure the robustness of the aggregation process, we design a novel softmax aggregation method based on approximated population loss values. Additionally, we show our softmax aggregation technique converges to the global minimum in the convex setting with non-restricting assumptions. Our comprehensive experiments show that our framework outperforms existing robust aggregation algorithms in various settings by large margins.

【Keywords】Federated learning; Blockchain; Robust aggregation

【发表时间】2024

【收录时间】2024-10-21

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-联邦学习