CoAst：基于跨轮估值的联邦学习免验证贡献评估
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation
AI总结：
本文提出免验证联邦学习贡献评估方法 CoAst，通过权重量化和跨轮参数相似性估值，在不依赖验证数据的情况下实现与基于验证方法相当、优于现有免验证方法的评估可靠性。
摘要：
在联邦学习（FL）过程中，由于每个参与者所持有的数据各不相同，因此有必要弄清楚哪个参与者对模型性能的贡献更大。有效的贡献评估有助于激励数据所有者参与联邦学习训练。该领域的研究工作可根据是否需要验证数据集分为两个方向。基于验证的方法需要使用具有代表性的验证数据来衡量模型精度，而这在实际联邦学习场景中很难获得。现有的免验证方法基于单个训练轮次中本地模型和全局模型的参数与梯度来评估贡献，容易受到模型训练随机性的干扰。本文提出 CoAst，一种无需任何验证数据即可评估联邦学习参与者贡献的实用方法。CoAst 的核心思想包含两个方面：一是通过权重量化仅统计模型参数中最重要的部分，二是基于当前本地参数与后续若干通信轮次中全局参数更新之间的相似性进行跨轮估值。大量实验表明，CoAst 的评估可靠性与现有基于验证的方法相当，并优于现有免验证方法。
英文摘要：
In the federated learning (FL) process, since the data held by each participant is different, it is necessary to figure out which participant has a higher contribution to the model performance. Effective contribution assessment can help motivate data owners to participate in the FL training. Research works in this field can be divided into two directions based on whether a validation dataset is required. Validation-based methods need to use representative validation data to measure the model accuracy, which is difficult to obtain in practical FL scenarios. Existing validation-free methods assess the contribution based on the parameters and gradients of local models and the global model in a single training round, which is easily compromised by the stochasticity of model training. In this work, we propose CoAst, a practical method to assess the FL participants' contribution without access to any validation data. The core idea of CoAst involves two aspects: one is to only count the most important part of model parameters through a weights quantization, and the other is a cross-round valuation based on the similarity between the current local parameters and the global parameter updates in several subsequent communication rounds. Extensive experiments show that CoAst has comparable assessment reliability to existing validation-based methods and outperforms existing validation-free methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
