Federated learning (FL) is a privacy-preserving distributed machine learning framework, which involves training statistical models over a number of mobile users (i.e., workers) while keeping data localized. However, recent works have demonstrated that workers engaged in FL are still susceptible to advanced inference attacks when sharing model updates or gradients, which would discourage them from participating. Most of the existing incentive mechanisms for FL mainly account for workers’ resource cost, while the cost incurred by potential privacy leakage resulting from inference attacks has rarely been incorporated. To address these issues, in this paper, we propose a contract-based personalized privacy-preserving incentive for FL, named Pain-FL, to provide customized payments for workers with different privacy preferences as compensation for privacy leakage cost while ensuring satisfactory convergence performance of FL models. The core idea of Pain-FL is that each worker agrees on a customized contract, which specifies a kind of privacy-preserving level (PPL) and the corresponding payment, with the server in each round of FL. Then, the worker perturbs her calculated stochastic gradients to be uploaded with that PPL in exchange for that payment. In particular, we respectively derive a set of optimal contracts analytically under both complete and incomplete information models, which could optimize the convergence performance of the finally learned global model, while bearing some desired economic properties, i.e., budget feasibility, individual rationality, and incentive compatibility. An exhaustive experimental evaluation of Pain-FL is conducted, and the results corroborate its practicability and effectiveness.

中文翻译：

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Pain-FL：联邦学习的个性化隐私保护激励


联邦学习 (FL) 是一种保护隐私的分布式机器学习框架，涉及对大量移动用户（即工作人员）训练统计模型，同时保持数据本地化。然而，最近的研究表明，从事 FL 的工作人员在共享模型更新或梯度时仍然容易受到高级推理攻击，这会阻止他们参与。现有的FL激励机制大多主要考虑工人的资源成本，而很少考虑推理攻击导致的潜在隐私泄露所产生的成本。为了解决这些问题，在本文中，我们提出了一种基于合约的 FL 个性化隐私保护激励，名为 Pain-FL，为具有不同隐私偏好的工作人员提供定制支付，作为隐私泄露成本的补偿，同时确保令人满意的收敛性能。 FL 型号。 Pain-FL的核心思想是，每轮FL中，每个worker都与服务器商定一份定制的合约，该合约指定了一种隐私保护级别（PPL）以及相应的支付费用。然后，工作人员扰乱她计算出的随机梯度，将其与该 PPL 一起上传以换取该付款。特别是，我们分别在完全和不完全信息模型下分析得出一组最优契约，可以优化最终学习的全局模型的收敛性能，同时具有一些期望的经济属性，即预算可行性、个体理性和激励兼容性。对Pain-FL进行了详尽的实验评估，结果证实了其实用性和有效性。