提出快速移除联邦学习中客户端知识的新方法，提升隐私合规效率。
Towards Robust Knowledge Removal in Federated Learning with High Data Heterogeneity
- 利用任务算术与神经正切核实现单轮快速知识清除
- 无需多轮通信，显著缩短模型不可用时间
- 适合对隐私删除时效性要求高的工业级联邦学习系统
如今，大量便携设备具备采集海量数据和较强计算能力的能力，为分布式训练人工智能模型提供了可能，同时保护参与客户端的隐私。然而，由于隐私法规和安全要求，必要时需消除某客户端对模型的贡献。该清除过程必须满足特定有效性与时效性要求。近年来，已有研究提出多种知识移除方法，但均需数据持有者与协调方多次通信，可能导致模型在移除完成前长期不可用，影响系统用户服务。本文提出一种基于任务算术与神经正切核的创新解决方案，可在单轮通信内快速移除客户端的影响，显著缩短模型不可用时间。
原文摘要 · Abstract (English)
Nowdays, there are an abundance of portable devices capable of collecting large amounts of data and with decent computational power. This opened the possibility to train AI models in a distributed manner, preserving the participating clients' privacy. However, because of privacy regulations and safety requirements, elimination upon necessity of a client contribution to the model has become mandatory. The cleansing process must satisfy specific efficacy and time requirements. In recent years, research efforts have produced several knowledge removal methods, but these require multiple communication rounds between the data holders and the process coordinator. This can cause the unavailability of an effective model up to the end of the removal process, which can result in a disservice to the system users. In this paper, we introduce an innovative solution based on Task Arithmetic and the Neural Tangent Kernel, to rapidly remove a client's influence from a model.
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