Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable collaborative learning while protecting local data and models. However, previous works show that parties without labels (passive parties) in VFL can infer the sensitive label or feature information owned by the party with labels (active party), or execute backdoor attacks. Meanwhile, active party can also infer sensitive feature or attribute information from passive party. All these pose great challenges to VFL systems. Former defense methods tend to experience either a loss in overall effectiveness or are too specialized for specific tasks. In this work, we propose a novel method and a unified framework for defending various attacks in VFL altogether, namely Mutual Information Regularization Defense (MID), which limits the mutual information between private raw data and intermediate outputs to achieve a consistently better trade-off between model utility and privacy. We provide both theoretical and experimental evidence to confirm the effectiveness of our MID framework in defending against a wide range of label and feature inference attacks, along with backdoor attacks in VFL. These showcase its promising potential as a versatile and effective defense mechanism, not tied to any specific task.

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters. Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy. We provide an exhaustive categorization for VFL settings and privacy-preserving protocols and comprehensively analyze the privacy attacks and defense strategies for each protocol. In the end, we propose a unified framework, termed VFLow, which considers the VFL problem under communication, computation, privacy, as well as effectiveness and fairness constraints. Finally, we review the most recent advances in industrial applications, highlighting open challenges and future directions for VFL.

Modern Internet of Things (IoT) systems are highly complex due to its mobile, ad-hoc and geographically distributed nature. Very often, an edge-cloud infrastructure is established to offer intelligent services in modern IoT systems. However, IoT edge devices are typically resource-constrained and can not perform sophisticated machine learning algorithm on board. Data sharing with a central server is a common approach of crowdsourcing, but also brings privacy and security concerns. The emerging federated learning offers a promising pathway to achieve an accurate model through distributed machine learning while ensuring data privacy. The existing federated learning process is not tailored to the mobile and adhoc nature of IoT systems where devices are of varying data and system qualities and may not be able to participate the entire training process. Therefore, in this paper, a new federated learning framework is proposed to support asynchronous model fusion with clustering-based participant selection. The proposed framework aims to accommodate the ad-hoc nature of IoT devices, and at the same time avoiding low quality or even malicious data from its participants to ensure model convergence and performance.

With the development of IT infrastructures, applications and systems generate a tsunami of data that keeps growing. Traditional IT management solutions can’t keep up with volume and complexity. Artificial intelligence for IT operations (AIOps) is an extremely effective method that could simplify IT operations management and accelerate & automate problem resolution in complex modern IT environments. Alarm root cause location is an important scenario and key function of AIOps. At present, the relevant research work mainly focuses on the association mining of historical alarm data, forming alarm rules, and processing offline alarm compression. However, the practical applications require faster and more accurate root cause location of alarms, which could put forward higher requirements for its real-time performance. The online real-time alarm root cause location algorithm proposed in this paper can fully explore the relationship between alarms in the dimension of time and space, and achieve online alarm data compression through technologies such as alarm association time, alarm event division, and alarm event topology generation. Real-time accurate division of alarm events and real-time location of key alarms greatly improve the velocity and accuracy of root cause location. The algorithm has been launched on a mobile network operator's 5G network management system. With the application of the proposed algorithm, over 10,000 alarms are processed per minute, and the accuracy of the root cause of the alarm has reached 85%, which has achieved good online effects.

Data security policies have significant impacts on big data and artificial intelligence applications, and yield data silo due to the data inaccessibility among the commercial companies for privacy-preserving. On September 1st, 2021, China officially implemented the Data Security Law. This paper timely proposes, validates, and productizes a trusted data sharing solution based on Vertical Federated Learning (VFL) technology across telecom and finance companies. A VFL model with Hetero Secure Boost Tree (HSBT) algorithm is proposed to overcome the data silo problems and to preserve user data privacy. Experimental results demonstrate that the model with both financial and telecom data improves the success rate of marketing for a tier-1 commercial bank in China. The solution has also been successfully implemented to greatly improve the marketing efficiency, resulting an approximately 50% cost reduction.