CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesters

• URL: http://arxiv.org/abs/2504.15847v1
• Date: Tue, 22 Apr 2025 12:42:45 GMT
• Title: CARE: Compatibility-Aware Incentive Mechanisms for Federated Learning with Budgeted Requesters
• Authors: Xiang Liu, Hau Chan, Minming Li, Xianlong Zeng, Chenchen Fu, Weiwei Wu,
• Abstract summary: Federated learning (FL) allows requesters to obtain local training models from workers (e.g., clients)<n>In this paper, we investigate the scenario in FL where multiple budgeted requesters seek training services from incompatible workers with private training costs.<n>We develop novel compatibility-aware incentive mechanisms, CARE-CO and CARE-NO, for both settings to elicit true private costs and determine workers to hire for requesters and their rewards.
• Score: 25.69656488516605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is a promising approach that allows requesters (\eg, servers) to obtain local training models from workers (e.g., clients). Since workers are typically unwilling to provide training services/models freely and voluntarily, many incentive mechanisms in FL are designed to incentivize participation by offering monetary rewards from requesters. However, existing studies neglect two crucial aspects of real-world FL scenarios. First, workers can possess inherent incompatibility characteristics (e.g., communication channels and data sources), which can lead to degradation of FL efficiency (e.g., low communication efficiency and poor model generalization). Second, the requesters are budgeted, which limits the amount of workers they can hire for their tasks. In this paper, we investigate the scenario in FL where multiple budgeted requesters seek training services from incompatible workers with private training costs. We consider two settings: the cooperative budget setting where requesters cooperate to pool their budgets to improve their overall utility and the non-cooperative budget setting where each requester optimizes their utility within their own budgets. To address efficiency degradation caused by worker incompatibility, we develop novel compatibility-aware incentive mechanisms, CARE-CO and CARE-NO, for both settings to elicit true private costs and determine workers to hire for requesters and their rewards while satisfying requester budget constraints. Our mechanisms guarantee individual rationality, truthfulness, budget feasibility, and approximation performance. We conduct extensive experiments using real-world datasets to show that the proposed mechanisms significantly outperform existing baselines.

Related papers

• Regularized Proportional Fairness Mechanism for Resource Allocation Without Money [18.593061465167363]
  We design an innovative neural network architecture tailored to the resource allocation problem, which we name Regularized Proportional Fairness Network (RPF-Net)<n>RPF-Net regularizes the output of the proportional fairness (PF) mechanism by a learned function approximator of the most exploitable allocation.<n>We derive generalization bounds that guarantee the mechanism performance when trained under finite and out-of-distribution samples.
  arXiv  Detail & Related papers  (2025-01-02T07:17:23Z)
• Incentivized Truthful Communication for Federated Bandits [61.759855777522255]
  We propose an incentive compatible (i.e., truthful) communication protocol, named Truth-FedBan. We show that Truth-FedBan still guarantees the sub-linear regret and communication cost without any overheads.
  arXiv  Detail & Related papers  (2024-02-07T00:23:20Z)
• Learning Payment-Free Resource Allocation Mechanisms [19.60309632660988]
  We consider the design of mechanisms that limited resources among self-interested agents using neural networks. We contribute a new end-to-end neural network architecture, ExS-Net, that accommodates the idea of "money-burning" for mechanism design without payments.
  arXiv  Detail & Related papers  (2023-11-18T01:21:54Z)
• 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)
• Online Learning under Budget and ROI Constraints via Weak Adaptivity [57.097119428915796]
  Existing primal-dual algorithms for constrained online learning problems rely on two fundamental assumptions. We show how such assumptions can be circumvented by endowing standard primal-dual templates with weakly adaptive regret minimizers. We prove the first best-of-both-worlds no-regret guarantees which hold in absence of the two aforementioned assumptions.
  arXiv  Detail & Related papers  (2023-02-02T16:30:33Z)
• 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)
• 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)
• Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima [114.31577038081026]
  We propose an efficient method that tackles the designer's and agents' problems simultaneously in a single loop. Although the designer does not solve the equilibrium problem repeatedly, it can anticipate the overall influence of the incentives on the agents. We prove that the algorithm converges to the global optima at a sublinear rate for a broad class of games.
  arXiv  Detail & Related papers  (2021-10-04T06:53:59Z)
• 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)
• Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning [100.73223416589596]
  We propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations. A new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-06T06:28:17Z)

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.