增加客户端数量会降低联邦学习精度，引入少量知识型客户端可显著提升效果。
The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
- 通过引入少量数据丰富的知识型客户端改善联邦学习
- 客户端数量增多时，传统FedAvg精度显著下降
- 适合关注隐私保护与边缘计算的实践者
联邦学习（FL）在保持数据本地化的同时训练共享模型，广泛应用于移动边缘计算、物联网和跨孤岛场景。本文聚焦常用的FedAvg算法，发现客户端数量增加会导致学习精度显著下降。为应对这一问题，提出知识型客户端插入（KCI）方法，仅引入极少量数据丰富的知识型客户端即可显著提升训练效率。即使使用标准的FedAvg聚合方式，也能加快收敛速度。该方法有望增强对模型反演攻击等安全威胁的隐私保护能力。代码已公开于https://github.com/Eleanor-W/KCI_for_FL。
原文摘要 · Abstract (English)
Federated Learning (FL) has been introduced as a way to keep data local to clients while training a shared machine learning model, as clients train on their local data and send trained models to a central aggregator. It is expected that FL will have a huge implication on Mobile Edge Computing, the Internet of Things, and Cross-Silo FL. In this paper, we focus on the widely used FedAvg algorithm to explore the effect of the number of clients in FL. We find a significant deterioration of learning accuracy for FedAvg as the number of clients increases. To address this issue for a general application, we propose a method called Knowledgeable Client Insertion (KCI) that introduces a very small number of knowledgeable clients to the MEC setting. These knowledgeable clients are expected to have accumulated a large set of data samples to help with training. With the help of KCI, the learning accuracy of FL increases much faster even with a normal FedAvg aggregation technique. We expect this approach to be able to provide great privacy protection for clients against security attacks such as model inversion attacks. Our code is available at https://github.com/Eleanor-W/KCI_for_FL.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。