The Force of Compensation, a Multi-stage Incentive Mechanism Model for Federated Learning

Han Xu, Priyadarsi Nanda, Jie Liang, Xiangjian He

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


In federated learning, data owners ‘provide’ their local data to model owners to train a mature model in a privacy-preserving way. A critical factor in the success of a federated learning scheme is an optimal incentive mechanism that motivates all participants to fully contribute. However, the privacy protection inherent to federated learning creates a dual ethical risk problem in that there is information asymmetry between the two parties, so neither side’s effort is observable. Additionally, there is often an implicit cost associated with the effort contributed to training a model, which may lead to self-interested, opportunistic behaviour on both sides. Existing incentive mechanisms have not addressed this issue. Hence, in this paper, we analyse how dual ethical risk affects the performance of federated learning schemes. We also derive an optimal multi-stage contract-theoretic incentive mechanism that minimises this risk, and experiment with calculating an optimal incentive contract for all participants. To our best knowledge, this is the first time that dual ethical risk for federated learning participants has been discussed. It is also the first time that an optimal incentive mechanism to overcome this issue has been developed.

Original languageEnglish
Title of host publicationNetwork and System Security - 16th International Conference, NSS 2022, Proceedings
EditorsXingliang Yuan, Guangdong Bai, Cristina Alcaraz, Suryadipta Majumdar
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783031230196
Publication statusPublished - 2022
Event16th International Conference on Network and System Security, NSS 2022 - Denarau Island, Fiji
Duration: 9 Dec 202212 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13787 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Network and System Security, NSS 2022
CityDenarau Island


  • Ethical risk
  • Federated learning
  • Incentive mechanism

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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