Federated learning (FL) has emerged as a promising master/slave learning paradigm to alleviate systemic privacy risks and communication costs incurred by cloud-centric machine learning methods. However, it is very challenging to resist the single point of failure of the master aggregator and attacks from malicious participants while guaranteeing model convergence speed and accuracy. Recently, blockchain has been brought into FL systems transforming the paradigm to a decentralized manner thus further improve the system security and learning reliability. Unfortunately, the traditional consensus mechanism and architecture of blockchain systems can hardly handle the large-scale FL task due to the huge resource consumption, limited transaction throughput, and high communication complexity. To address these issues, this paper proposes a two-layer blockchaindriven FL framework, called as ChainsFL, which is composed of multiple subchain networks (subchain layer) and a direct acyclic graph (DAG)-based mainchain (mainchain layer). In ChainsFL, the subchain layer limits the scale of each shard for a small range of information exchange, and the mainchain layer allows each shard to share and validate the learning model in parallel and asynchronously to improve the efficiency of cross-shard validation. Furthermore, the FL procedure is customized to deeply integrate with blockchain technology, and the modified DAG consensus mechanism is proposed to mitigate the distortion caused by abnormal models. In order to provide a proof-ofconcept implementation and evaluation, multiple subchains base on Hyperledger Fabric are deployed as the subchain layer, and the self-developed DAG-based mainchain is deployed as the mainchain layer. The experimental results show that ChainsFL provides acceptable and sometimes better training efficiency and stronger robustness compared with the typical existing FL systems.
翻译:联邦学习(FL)已成为一个大有希望的主/奴隶学习模式,可以减轻以云为中心的机器学习方法造成的系统性隐私风险和通信成本,但抵御主聚合器单一的失败点和恶意参与者的袭击,同时保证模式趋同速度和准确性,是非常具有挑战性的;最近,将链条引入FL系统,将范式转化为分散方式,从而进一步改善系统安全和学习可靠性;不幸的是,由于资源消耗巨大、交易量有限、通信复杂性高,传统共识机制和链链锁系统架构难以处理大型FL任务;为解决这些问题,本文提议了一个双层链驱动的FL框架,称为链式链链,由多链链网络(次链层)和直接环环状图(DAG)构成,从而进一步改善系统安全性和学习可靠性;在链链条中,链链子限制每个碎片的大小,用于进行小规模的信息交流,而主链链条则使每个学习模式在平行和连续链路中更好地分享和验证学习模式;在系统内部和内部链路段上,以链路路路为链条驱动的自我调节,从而改进了成本化的系统,从而改进了目前对Fl-L的系统进行升级的升级的升级的升级的升级的升级,从而改进了目前的系统,从而降低了的升级,从而改进了现有和深级的升级化的系统,从而改进了FL-L-L-L-级的升级的系统,从而改进了FL的升级的系统,从而改进了目前的系统,从而改进了目前的系统,从而改进了目前的系统,从而降低了了目前的系统,从而降低了了目前的系统。