Blockchain Assisted Federated Learning Over Wireless Channels: Dynamic Resource Allocation and Client Scheduling

【Author】 Deng, Xiumei; Li, Jun; Ma, Chuan; Wei, Kang; Shi, Long; Ding, Ming; Chen, Wen; Poor, H. Vincent

【Source】IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS

【影响因子】8.346

【Abstract】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, 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. To optimize the learning performance of FL, we propose to maximize the long-term time average (LTA) training data size under a constraint of LTA energy consumption. To this end, we formulate a joint optimization problem of training client selection and resource allocation (i.e., the transmit power and computation frequency at the client side), and solve the long-term mixed integer non-linear program based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [ $\mathcal {O}(1/V)$ , $\mathcal {O}(\sqrt {V})$ ] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter $V$ . Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption.

【Keywords】Computational modeling; Blockchains; Training; Wireless communication; Energy consumption; Resource management; Dynamic scheduling; Federated learning; blockchain; Lyapunov optimization; resource allocation; client scheduling

【发表时间】2023 MAY

【收录时间】2023-07-03

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