Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement

• URL: http://arxiv.org/abs/2201.02410v1
• Date: Fri, 7 Jan 2022 11:44:20 GMT
• Title: Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement
• Authors: Jingwen Zhang, Yuezhou Wu, Rong Pan
• Abstract summary: Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML. We design an auction-based incentive mechanism for horizontal federated learning with reputation and contribution measurement.
• Score: 9.503584357135832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML. A large number of workers with data and computing power are the foundation of federal learning. However, the inevitable costs prevent self-interested workers from serving for free. Moreover, due to data isolation, task publishers lack effective methods to select, evaluate and pay reliable workers with high-quality data. Therefore, we design an auction-based incentive mechanism for horizontal federated learning with reputation and contribution measurement. By designing a reasonable method of measuring contribution, we establish the reputation of workers, which is easy to decline and difficult to improve. Through reverse auctions, workers bid for tasks, and the task publisher selects workers combining reputation and bid price. With the budget constraint, winning workers are paid based on performance. We proved that our mechanism satisfies the individual rationality of the honest worker, budget feasibility, truthfulness, and computational efficiency.

Related papers

• Blockchain-enabled Trustworthy Federated Unlearning [50.01101423318312]
  Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients. Existing works require central servers to retain the historical model parameters from distributed clients. This paper proposes a new blockchain-enabled trustworthy federated unlearning framework.
  arXiv  Detail & Related papers  (2024-01-29T07:04:48Z)
• Incentivizing Massive Unknown Workers for Budget-Limited Crowdsensing: From Off-Line and On-Line Perspectives [31.24314338983544]
  We propose an off-line Context-Aware CMAB-based Incentive (CACI) mechanism. We also extend the idea to the on-line setting where unknown workers may join in or depart from the systems.
  arXiv  Detail & Related papers  (2023-09-21T14:30:42Z)
• Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning [0.12289361708127873]
  Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. How to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. We propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result.
  arXiv  Detail & Related papers  (2023-09-21T01:47:39Z)
• Cost-efficient Crowdsourcing for Span-based Sequence Labeling: Worker Selection and Data Augmentation [30.179968217703635]
  This paper introduces a novel crowdsourcing worker selection algorithm, enhancing annotation quality and reducing costs. The proposed algorithm utilizes a Combinatorial Multi-Armed Bandit (CMAB) approach for worker selection, and a cost-effective human feedback mechanism.
  arXiv  Detail & Related papers  (2023-05-11T09:40:24Z)
• Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework [68.8725783112254]
  We address the problem of designing incentive mechanisms by a virtual service provider (VSP) to hire sensing IoT devices to sell their sensing data. Due to the limited bandwidth, we propose to use semantic extraction algorithms to reduce the delivered data by the sensing IoT devices. We propose a novel iterative contract design and use a new variant of multi-agent reinforcement learning (MARL) to solve the modelled multi-dimensional contract problem.
  arXiv  Detail & Related papers  (2023-02-22T15:52:37Z)
• Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective [1.749935196721634]
  Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models. The economic considerations of the clients, such as fairness and incentive, are yet to be fully explored. We propose a novel incentive mechanism that involves a client selection process to remove low-quality clients and a money transfer process to ensure a fair reward distribution.
  arXiv  Detail & Related papers  (2023-02-17T16:31:19Z)
• Understanding Information Disclosure from Secure Computation Output: A Study of Average Salary Computation [58.74407460023331]
  Quantifying information disclosure about private inputs from observing a function outcome is the subject of this work. Motivated by the City of Boston gender pay gap studies, in this work we focus on the computation of the average of salaries.
  arXiv  Detail & Related papers  (2022-09-21T15:59:48Z)
• Mechanisms that Incentivize Data Sharing in Federated Learning [90.74337749137432]
  We show how a naive scheme leads to catastrophic levels of free-riding where the benefits of data sharing are completely eroded. We then introduce accuracy shaping based mechanisms to maximize the amount of data generated by each agent.
  arXiv  Detail & Related papers  (2022-07-10T22:36:52Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint [9.503584357135832]
  Federated learning makes it possible for all parties with data isolation to train the model collaboratively and efficiently. To obtain a high-quality model, an incentive mechanism is necessary to motivate more high-quality workers with data and computing power. We propose a reverse auction-based online incentive mechanism for horizontal federated learning with budget constraint.
  arXiv  Detail & Related papers  (2022-01-22T13:37:52Z)
• Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing [55.012801269326594]
  In Byzantine robust distributed learning, a central server wants to train a machine learning model over data distributed across multiple workers. A fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages. We propose a simple bucketing scheme that adapts existing robust algorithms to heterogeneous datasets at a negligible computational cost.
  arXiv  Detail & Related papers  (2020-06-16T17:58:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.