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 these traditional models’ efficiency and privacy challenges, 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. Therefore, 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 given 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, in terms of model accuracy (13%, 16%, and 26%), communication cost (17%, 21%, and 25%), and computation cost (7%, 9%, and 11%) improvements over existing approaches. The FedAnil+ code is available on GitHub.

中文翻译：

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一种轻量级、安全的深度学习模型，用于智能企业中保护隐私的联邦学习


不断增长的物联网 (IoT) 连接催生了一种新型组织：智能企业。在智能企业中，采用基于机器学习的模型从数据中提取洞察。由于这些传统模型的效率和隐私挑战，出现了一种新的联邦学习（FL）范式。在FL中，多个企业可以联合训练一个模型来更新最终的模型。然而，首先，FL 训练的模型通常比集中式模型的性能更差，特别是当企业的训练数据是非 IID（独立同分布）时。其次，由于FL的中心性和本土企业的不可信性，传统的FL解决方案容易受到投毒和推理攻击，侵犯隐私。第三，企业与服务器之间不断传递参数，增加了通信成本。因此，为此，提出了FedAnil+模型，这是一种新颖、轻量级且安全的联邦深度学习模型，包括三个主要阶段。第一阶段的目标是解决数据类型分布倾斜的挑战。第二阶段将解决针对中毒和推理攻击的隐私问题。最后，为了减轻通信开销，提出了一种新颖的压缩方法，可以显着减小更新的大小。实验结果验证了FedAnil+能够以更高的准确度抵御推理和投毒攻击。此外，在模型准确度（13%、16%和26%）、通信成本（17%、21%和25%）和计算成本（7%、9%和11%）方面比现有的方法。 FedAnil+ 代码可在 GitHub 上获取。