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 compared to traditional Federated Learning algorithms. 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 that relies on our specific blockchain architecture. 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.

翻译：基于区块链的联邦学习是一种分布式学习方案，允许参与方在不共享本地数据集的情况下训练模型。与传统联邦学习算法相比，区块链组件消除了对可信中央服务器的需求。本文提出了一种基于Softmax聚合的区块链联邦学习框架。首先，我们设计了一种新的基于区块链的联邦学习架构，利用现有区块链网络上经过充分验证的权益证明共识机制来选择验证者和矿工，以聚合参与方更新并计算区块。其次，为确保聚合过程的鲁棒性，我们基于近似总体损失值提出了一种新颖的Softmax聚合方法，该方法依赖于我们特定的区块链架构。此外，我们证明了在无限制假设的凸优化设定下，所提出的Softmax聚合技术能够收敛到全局最小值。大量实验表明，我们的框架在各种设定下均以显著优势优于现有鲁棒聚合算法。