On Dynamic Resource Allocation for Blockchain Assisted Federated Learning over Wireless Channels

【Author】 Deng, Xiumei; Li, Jun; Shi, Long; Wang, Zhe; Wang, Jessie Hui; Wang, Taotao

【Source】IEEE CONGRESS ON CYBERMATICS / 2021 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS (ITHINGS) / IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) / IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) / IEEE SMART DATA (SMARTDATA)

【Abstract】The blockchain technology has been extensively studied to enable distributed and tamper-proof data processing in federated learning (FL). Most existing blockchain assisted FL (BFL) frameworks have employed a third-party blockchain network to decentralize the model aggregation process. However, the decentralized model aggregation is vulnerable to pooling and collusion attacks from the third-party blockchain network. Driven by this issue, we propose a novel BFL framework that features the integration of training and mining at the client side. In our framework, the client first transmits its trained model to other clients, performs global aggregation upon receiving others' models, and competes to mine a block for aggregated model verification without the intervention of any third-party blockchain network. Considering the model transmission over time-varying wireless channels, we propose a dynamic training client scheduling to meet stringent latency requirement in FL, where clients with qualified channel conditions are scheduled to train their models in each communication round. Furthermore, we formulate a joint optimization problem of the training client scheduling and dynamic resource allocation (i.e., the transmit and computation power at the client side) under the constraint of long-term time-average (LTA) energy consumption. The objective of this optimization problem is to maximize the LTA training data size and thereby optimize the learning performance of FL. To this end, we obtain the closed-form solution by using the Lyapunov optimization method. Our experimental results show that the optimal solution can outperform baseline schemes in terms of learning accuracy and convergence time.

【Keywords】Federated learning; blockchain; Lyapunov optimization; resource allocation; client scheduling

【标题】基于无线信道的区块链辅助联邦学习的动态资源分配

【摘要】区块链技术已被广泛研究，以实现联邦学习 (FL) 中的分布式和防篡改数据处理。大多数现有的区块链辅助 FL (BFL) 框架都采用了第三方区块链网络来分散模型聚合过程。然而，去中心化模型聚合容易受到来自第三方区块链网络的池化和合谋攻击。在这个问题的驱动下，我们提出了一种新颖的 BFL 框架，其特点是在客户端集成了训练和挖掘。在我们的框架中，客户端首先将其训练好的模型传输给其他客户端，在接收到其他客户端的模型后进行全局聚合，并在没有任何第三方区块链网络干预的情况下竞争挖掘区块进行聚合模型验证。考虑到随时间变化的无线信道上的模型传输，我们提出了一种动态训练客户端调度，以满足 FL 中严格的延迟要求，其中具有合格信道条件的客户端被调度在每个通信轮次中训练他们的模型。此外，我们制定了在长期时间平均（LTA）能耗约束下的训练客户端调度和动态资源分配（即客户端的传输和计算能力）的联合优化问题。这个优化问题的目标是最大化 LTA 训练数据的大小，从而优化 FL 的学习性能。为此，我们利用李雅普诺夫优化方法得到了闭式解。我们的实验结果表明，最优解在学习精度和收敛时间方面可以优于基线方案。

【关键词】联邦学习；区块链；李雅普诺夫优化；资源分配;客户调度

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Proceedings Paper

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【翻译者】石东瑛