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

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

Research output: Contribution to journalArticlepeer-review


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 programming based on a Lyapunov technique. In particular, the proposed dynamic resource allocation and client scheduling (DRACS) algorithm can achieve a trade-off of [<italic>O</italic>(1/<italic>V</italic>), <italic>O</italic>(&#x221A;<italic>V</italic>)] to balance the maximization of the LTA training data size and the minimization of the LTA energy consumption with a control parameter <italic>V</italic>. Our experimental results show that the proposed DRACS algorithm achieves better learning accuracy than benchmark client scheduling strategies with limited time or energy consumption.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Wireless Communications
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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


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