提出轻量安全的联邦学习模型，解决企业数据异构与隐私泄露问题。
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises
- 分三阶段设计：缓解数据分布偏移、抵御攻击、压缩通信更新
- 准确率提升13%-26%，通信与计算开销降低17%-25%
- 适合注重隐私与效率的智能企业部署
物联网连接激增催生了智能企业新形态。此类企业依赖机器学习从数据中提取洞察，但传统模型面临效率低与隐私风险。联邦学习（FL）虽可协同训练，却存在三大挑战：数据非独立同分布（non-IID）导致性能下降；中心化架构易受投毒与推断攻击；频繁参数传输增加通信成本。为此，本文提出FedAnil+模型，包含三个核心阶段：第一阶段解决数据分布偏移问题；第二阶段强化对投毒与推断攻击的隐私保护；第三阶段引入新型压缩方法显著减少更新数据量。实验表明，FedAnil+在安全性上有效抵御攻击，且相比现有方法，在模型准确率（提升13%、16%、26%）、通信成本（降低17%、21%、25%）和计算开销（减少7%、9%、11%）方面均实现显著优化。
原文摘要 · Abstract (English)
The ever growing Internet of Things (IoT) connections drive a new type of organization, the Intelligent Enterprise. In intelligent enterprises, machine learning based models are adopted to extract insights from data. Due to the efficiency and privacy challenges of these traditional models, a new federated learning (FL) paradigm has emerged. In FL, multiple enterprises can jointly train a model to update a final model. However, firstly, FL trained models usually perform worse than centralized models, especially when enterprises training data is non-IID (Independent and Identically Distributed). Second, due to the centrality of FL and the untrustworthiness of local enterprises, traditional FL solutions are vulnerable to poisoning and inference attacks and violate privacy. Thirdly, the continuous transfer of parameters between enterprises and servers increases communication costs. To this end, the FedAnil+ model is proposed, a novel, lightweight, and secure Federated Deep Learning Model that includes three main phases. In the first phase, the goal is to solve the data type distribution skew challenge. Addressing privacy concerns against poisoning and inference attacks is covered in the second phase. Finally, to alleviate the communication overhead, a novel compression approach is proposed that significantly reduces the size of the updates. The experiment results validate that FedAnil+ is secure against inference and poisoning attacks with better accuracy. In addition, it shows improvements over existing approaches in terms of model accuracy (13%, 16%, and 26%), communication cost (17%, 21%, and 25%), and computation cost (7%, 9%, and 11%).
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。