Auction-based Federated Learning (AFL) has attracted extensive research
interest due to its ability to motivate data owners (DOs) to join FL through
economic means. While many existing AFL methods focus on providing decision
support to model users (MUs) and the AFL auctioneer, decision support for data
owners remains open. To bridge this gap, we propose a first-of-its-kind
agent-oriented joint Pricing, Acceptance and Sub-delegation decision support
approach for data owners in AFL (PAS-AFL). By considering a DO's current
reputation, pending FL tasks, willingness to train FL models, and its trust
relationships with other DOs, it provides a systematic approach for a DO to
make joint decisions on AFL bid acceptance, task sub-delegation and pricing
based on Lyapunov optimization to maximize its utility. It is the first to
enable each DO to take on multiple FL tasks simultaneously to earn higher
income for DOs and enhance the throughput of FL tasks in the AFL ecosystem.
Extensive experiments based on six benchmarking datasets demonstrate
significant advantages of PAS-AFL compared to six alternative strategies,
beating the best baseline by 28.77% and 2.64% on average in terms of utility
and test accuracy of the resulting FL models, respectively.

基于拍卖的联邦学习（AFL）引起了广泛的研究兴趣，因其能通过经济手段激励数据所有者（DO）参与 FL。本文提出了一种面向 DO 的首个代理导向的联合定价、接受和分委托决策支持方法（PAS-AFL），通过考虑 DO 的当前声誉、待处理的 FL 任务、愿意训练 FL 模型以及其与其他 DO 之间的信任关系，提供了一种系统的方法来进行 AFL 出价接受、任务分委托和定价的联合决策，并基于 Lyapunov 优化来最大化其效用。在六个基准数据集上进行的大量实验证明了 PAS-AFL 相比六种替代策略的重要优势，平均来说，在效用和最终 FL 模型的测试准确率方面，它分别比最佳基准线提高了 28.77% 和 2.64%。

面向代理的基于拍卖的联邦学习中数据拥有者的联合决策支持

Agent-oriented Joint Decision Support for Data Owners in Auction-based  Federated Learning

The rise of the phenomenon of the "right to be forgotten" has prompted
research on machine unlearning, which grants data owners the right to actively
withdraw data that has been used for model training, and requires the
elimination of the contribution of that data to the model. A simple method to
achieve this is to use the remaining data to retrain the model, but this is not
acceptable for other data owners who continue to participate in training.
Existing machine unlearning methods have been found to be ineffective in
quickly removing knowledge from deep learning models. This paper proposes using
a stochastic network as a teacher to expedite the mitigation of the influence
caused by forgotten data on the model. We performed experiments on three
datasets, and the findings demonstrate that our approach can efficiently
mitigate the influence of target data on the model within a single epoch. This
allows for one-time erasure and reconstruction of the model, and the
reconstruction model achieves the same performance as the retrained model.

使用随机网络作为教师，可以快速减轻被遗忘数据对模型的影响，实现模型的一次性擦除和重构，并取得与重新训练后模型相同的性能。