Prior-Free Auctions for the Demand Side of Federated Learning

• URL: http://arxiv.org/abs/2103.14375v1
• Date: Fri, 26 Mar 2021 10:22:18 GMT
• Title: Prior-Free Auctions for the Demand Side of Federated Learning
• Authors: Andreas Haupt and Vaikkunth Mugunthan
• Abstract summary: Federated learning allows distributed clients to learn a shared machine learning model without sharing their sensitive training data. We propose a mechanism, FIPFA, to collect monetary contributions from self-interested clients. We run experiments on the MNIST dataset to test clients' model quality under FIPFA and FIPFA's incentive properties.
• Score: 0.76146285961466
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Federated learning (FL) is a paradigm that allows distributed clients to learn a shared machine learning model without sharing their sensitive training data. While largely decentralized, FL requires resources to fund a central orchestrator or to reimburse contributors of datasets to incentivize participation. Inspired by insights from prior-free auction design, we propose a mechanism, FIPFA (Federated Incentive Payments via Prior-Free Auctions), to collect monetary contributions from self-interested clients. The mechanism operates in the semi-honest trust model and works even if clients have a heterogeneous interest in receiving high-quality models, and the server does not know the clients' level of interest. We run experiments on the MNIST dataset to test clients' model quality under FIPFA and FIPFA's incentive properties.

Related papers

• IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by Artificial Intelligence Generated Content [15.620004060097155]
  Federated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local data. We propose a data quality-aware incentive mechanism to encourage clients' participation. Our proposed mechanism exhibits highest training accuracy and reduces up to 53.34% of the server's cost with real-world datasets.
  arXiv  Detail & Related papers  (2024-06-12T07:47:22Z)
• An Auction-based Marketplace for Model Trading in Federated Learning [54.79736037670377]
  Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. We frame FL as a marketplace of models, where clients act as both buyers and sellers. We propose an auction-based solution to ensure proper pricing based on performance gain.
  arXiv  Detail & Related papers  (2024-02-02T07:25:53Z)
• Personalized Federated Learning with Attention-based Client Selection [57.71009302168411]
  We propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions. Experiments on CIFAR10 and FMNIST validate FedACS's superiority.
  arXiv  Detail & Related papers  (2023-12-23T03:31:46Z)
• Tunable Soft Prompts are Messengers in Federated Learning [55.924749085481544]
  Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources. The lack of model privacy protection in FL becomes an unneglectable challenge. We propose a novel FL training approach that accomplishes information exchange among participants via tunable soft prompts.
  arXiv  Detail & Related papers  (2023-11-12T11:01:10Z)
• Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation [31.2017942327673]
  This paper proposes a game theoretic incentive mechanism for federated learning (FL) with randomized client participation. We show that our mechanism achieves higher model performance for the server as well as higher profits for the clients.
  arXiv  Detail & Related papers  (2023-04-17T04:05:57Z)
• 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)
• FedToken: Tokenized Incentives for Data Contribution in Federated Learning [33.93936816356012]
  We propose a contribution-based tokenized incentive scheme, namely textttFedToken, backed by blockchain technology. We first approximate the contribution of local models during model aggregation, then strategically schedule clients lowering the communication rounds for convergence.
  arXiv  Detail & Related papers  (2022-09-20T14:58:08Z)
• Incentivizing Federated Learning [2.420324724613074]
  This paper presents an incentive mechanism that encourages clients to contribute as much data as they can obtain. Unlike previous incentive mechanisms, our approach does not monetize data. We theoretically prove that clients will use as much data as they can possibly possess to participate in federated learning under certain conditions.
  arXiv  Detail & Related papers  (2022-05-22T23:02:43Z)
• A Contract Theory based Incentive Mechanism for Federated Learning [52.24418084256517]
  Federated learning (FL) serves as a data privacy-preserved machine learning paradigm, and realizes the collaborative model trained by distributed clients. To accomplish an FL task, the task publisher needs to pay financial incentives to the FL server and FL server offloads the task to the contributing FL clients. It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients.
  arXiv  Detail & Related papers  (2021-08-12T07:30:42Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation [119.19061102064497]
  We propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL) In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, competes to generate a block based on the received models, and then aggregates the models from the generated block before its local training of the next round. We explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
  arXiv  Detail & Related papers  (2021-01-18T07:19:08Z)
• An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach [75.08185720590748]
  Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. We formulate the incentive mechanism between the BS and mobile users as an auction game where the BS is an auctioneer and the mobile users are the sellers.
  arXiv  Detail & Related papers  (2020-09-22T01:50:39Z)

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.