Introduction

区块链可以用于取代联邦学习中的中心聚合服务器

目前的区块链联邦学习中的区块链和参与联邦学习的用户独立,相当于服务器换成了一条区块链,也会有隐私泄漏的风险

目前的区块链联邦学习的工作缺乏对收敛性能和资源分配的考虑

本文做的工作是把挖矿任务也交给训练模型的用户,即用户即要训练模型又要进行挖矿.同时本文计算了改方法的收敛上界,并用实验结果证明上界分析的正确性.最优化了挖矿和训练两种行为的计算资源分配.同时考虑了lazy clients的情况,考虑了lazy client的存在对性能的影响

PRELIMINARIES

联邦学习:参与方共享本地数据训练的本地模型,保护隐私

区块链:去中心化的账本.每个区块包含交易记录,新的区块由计算出随机数nonce的矿工的账本生成

PROPOSED FRAMEWORK

BLADE-FL

Computing Resource Allocation Model

假设要在t_sum 时间内完成训练

PERFORMANCE ANALYSIS OF THE BLADE-FL SYSTEM

Achievable Upper Bound Analysis

和最优loss的差距上界,得出上界和K(联邦学习总轮次)有关

得出最小化上界的K值

EXPERIMENTAL RESULTS

Experimental Setting

dataset

MNIST,Fashion-MNIST,Cifar-10,SVHN 都是图像分类问题

Learning models.

result

实际实验也在相同的最优K的下达到最小loss

loss和K的关系图是凸关系

ming overhead 一般

训练效果最好

Conclusion

提出了BLADE-FL 框架, 让用户进行模型训练和挖矿工作, 避免了单点故障和隐私泄漏

分析了框架的损失函数的上界,验证了上界和联邦学习轮次K是凸关系,利用K达到上界最优化

实验验证了上界分析的正确性

未来工作

设计激励机制

识别lazy client

损失函数缺少凸性时的性能需要进一步分析

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