Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges

【Author】 Pokhrel, Shiva Raj; Choi, Jinho

【Source】IEEE TRANSACTIONS ON COMMUNICATIONS

【Abstract】We propose an autonomous blockchain-based federated learning (BFL) design for privacy-aware and efficient vehicular communication networking, where local on-vehicle machine learning (oVML) model updates are exchanged and verified in a distributed fashion. BFL enables oVML without any centralized training data or coordination by utilizing the consensus mechanism of the blockchain. Relying on a renewal reward approach, we develop a mathematical framework that features the controllable network and BFL parameters (e.g., the retransmission limit, block size, block arrival rate, and the frame sizes) so as to capture their impact on the system-level performance. More importantly, our rigorous analysis of oVML system dynamics quantifies the end-to-end delay with BFL, which provides important insights into deriving optimal block arrival rate by considering communication and consensus delays. We present a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive BFL design. In particular, based on analytical results, we minimize the system delay by exploiting the channel dynamics and demonstrate that the proposed idea of tuning the block arrival rate is provably online and capable of driving the system dynamics to the desired operating point. It also identifies the improved dependency on other blockchain parameters for a given set of channel conditions, retransmission limits, and frame sizes. (1) However, a number of challenges (gaps in knowledge) need to be resolved in order to realise these changes. In particular, we identify key bottleneck challenges requiring further investigations, and provide potential future reserach directions. (1) An early version of this work has been accepted for presentation in IEEE WCNC Wksps 2020 [1].

【Keywords】Delays; Blockchain; Computational modeling; Training; Autonomous vehicles; Servers; Machine learning; On-vehicle machine learning; federated learning; blockchain; delay analysis; consensus delay; low delay

【标题】用于自动驾驶汽车的区块链联邦学习：分析和设计挑战

【摘要】我们提出了一种基于自主区块链的联邦学习 (BFL) 设计，用于隐私感知和高效的车载通信网络，其中本地车载机器学习 (oVML) 模型更新以分布式方式进行交换和验证。 BFL 利用区块链的共识机制，无需任何集中的训练数据或协调即可实现 oVML。依靠更新奖励方法，我们开发了一个以可控网络和 BFL 参数（例如，重传限制、块大小、块到达率和帧大小）为特征的数学框架，以捕捉它们对系统级的影响表现。更重要的是，我们对 oVML 系统动力学的严格分析量化了 BFL 的端到端延迟，这为通过考虑通信和共识延迟来推导最佳块到达率提供了重要的见解。我们展示了各种数值和模拟结果，突出了自适应 BFL 设计的各种重要发现和见解。特别是，基于分析结果，我们通过利用信道动力学来最小化系统延迟，并证明所提出的调整块到达率的想法是在线的，并且能够将系统动力学驱动到所需的工作点。它还针对给定的一组通道条件、重传限制和帧大小确定了对其他区块链参数的改进依赖性。 (1) 然而，为了实现这些变化，需要解决许多挑战（知识差距）。特别是，我们确定了需要进一步调查的关键瓶颈挑战，并提供了潜在的未来研究方向。 (1) 这项工作的早期版本已被接受在 IEEE WCNC Wksps 2020 [1] 上发表。

【关键词】延误；区块链；计算建模；训练;自动驾驶汽车；服务器；机器学习；车载机器学习；联邦学习；区块链；延迟分析；共识延迟；低延迟

【发表时间】2020

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】6.166

【翻译者】石东瑛