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Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks

arXiv 2501.01584 ↗First posted 3 Jan 2025cs.LG cs.CR cs.GTcited by 0

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Abstract (arXiv)

Despite the advantage of preserving data privacy, federated learning (FL) still suffers from the straggler issue due to the limited computing resources of distributed clients and the unreliable wireless communication environment. By effectively imitating the distributed resources, digital twin (DT) shows great potential in alleviating this issue. In this paper, we leverage DT in the FL framework over non-orthogonal multiple access (NOMA) network to assist FL training process, considering malicious attacks on model updates from clients. A reputationbased client selection scheme is proposed, which accounts for client heterogeneity in multiple aspects and effectively mitigates the risks of poisoning attacks in FL systems. To minimize the total latency and energy consumption in the proposed system, we then formulate a Stackelberg game by considering clients and the server as the leader and the follower, respectively. Specifically, the leader aims to minimize the energy consumption while the objective of the follower is to minimize the total latency during FL training. The Stackelberg equilibrium is achieved to obtain the optimal solutions. We first derive the strategies for the followerlevel problem and include them in the leader-level problem which is then solved via problem decomposition. Simulation results verify the superior performance of the proposed scheme.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 3 Jan 2025.

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14 of 43 references.

References · 14

  1. 2021Convex optimizationcited by 2,479
  2. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  3. 2019Reliable Federated Learning for Mobile Networkscited by 92
  4. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  5. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  6. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  7. 2018Toward Understanding the Impact of Staleness in Distributed Machine Learningcited by 28
  8. 2020Adaptive Federated Learning and Digital Twin for Industrial Internet of Thingscited by 27
  9. 2023Real-Time Digital Twins: Vision and Research Directions for 6G and Beyondcited by 26
  10. 2018Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learningcited by 17
  11. 2021Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?cited by 13
  12. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
2 more
  1. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  2. 2021Wireless Federated Learning (WFL) for 6G Networks -- Part II: The Compute-then-Transmit NOMA Paradigmcited by 6

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