mlsanityData to 14 Oct 2026

Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks

arXiv 2212.11194 ↗First posted 21 Dec 2022cs.GT cs.AI cs.LGcited by 6

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Abstract (arXiv)

This paper presents a game theoretic framework for participation and free-riding in federated learning (FL), and determines the Nash equilibrium strategies when FL is executed over wireless links. To support spectrum sensing for NextG communications, FL is used by clients, namely spectrum sensors with limited training datasets and computation resources, to train a wireless signal classifier while preserving privacy. In FL, a client may be free-riding, i.e., it does not participate in FL model updates, if the computation and transmission cost for FL participation is high, and receives the global model (learned by other clients) without incurring a cost. However, the free-riding behavior may potentially decrease the global accuracy due to lack of contribution to global model learning. This tradeoff leads to a non-cooperative game where each client aims to individually maximize its utility as the difference between the global model accuracy and the cost of FL participation. The Nash equilibrium strategies are derived for free-riding probabilities such that no client can unilaterally increase its utility given the strategies of its opponents remain the same. The free-riding probability increases with the FL participation cost and the number of clients, and a significant optimality gap exists in Nash equilibrium with respect to the joint optimization for all clients. The optimality gap increases with the number of clients and the maximum gap is evaluated as a function of the cost. These results quantify the impact of free-riding on the resilience of FL in NextG networks and indicate operational modes for FL participation.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 21 Dec 2022.

Builds on

21 of 27 references.

References · 21

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2019Towards Federated Learning at Scale: System Designcited by 832
  4. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  5. 2018Analyzing Federated Learning through an Adversarial Lenscited by 351
  6. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  7. 2020Threats to Federated Learning: A Surveycited by 178
  8. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  9. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  10. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  11. 2019Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanismcited by 75
  12. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
9 more
  1. 2019Free-riders in Federated Learning: Attacks and Defensescited by 49
  2. 2021Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspectivecited by 47
  3. 2021Optimality and Stability in Federated Learning: A Game-theoretic Approachcited by 40
  4. 2020Deep Learning for Wireless Communicationscited by 39
  5. 2020A Secure Federated Learning Framework for 5G Networkscited by 32
  6. 2019Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environmentscited by 28
  7. 2022Federated Learning for Distributed Spectrum Sensing in NextG Communication Networkscited by 7
  8. 2022Jamming Attacks on Federated Learning in Wireless Networkscited by 6
  9. 2020Secure Federated Learning in 5G Mobile Networkscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 2.

6 papers.

  1. 2023Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networkscited by 4
  2. 2024Tackling Selfish Clients in Federated Learningcited by 3
  3. 2023Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solutioncited by 2
  4. 2025Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor Itcited by 0
  5. 2026Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theorycited by 0
  6. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0