Dealing with Noisy Data in Federated Learning: An Incentive Mechanism with Flexible Pricing

Hengzhi Wang, Haoran Chen, Minghe Ma, Laizhong Cui

Track: Systems and infrastructure for Web, mobile, and WoT

Keywords: Federated learning, noisy data, incentive mechanism, flexible pricing

Abstract: Federated Learning (FL) has emerged as a promising training framework that enables a server to effectively train a global model by coordinating multiple devices, i.e., clients, without sharing their raw data. Keeping data locally can ensure data privacy, but also makes the server difficult to assess data quality, leading to the noisy data issue. Specifically, for any given taring task, only a portion of each client's data is relevant and beneficial, while the rest may be redundant or noisy. Training with excessive noisy data can degrade performance. Motivated by this, we investigate the limitations of existing studies and develop an incentive mechanism with flexible pricing tailored for noisy data settings. The insight lies in mitigating the impact of noisy data by selecting appropriate clients and incentivizing them to clean their data spontaneously. Further, both rigorous theoretical analysis and extensive simulations compared with state-of-the-art methods have been well-conducted to validate the effectiveness of the proposed mechanism.

Submission Number: 1192