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.

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.

Mobile number portability (MNP) policy highlights the problem ofsubscriber churn and thus enhance the liquidity and competition of the telecommunication market. China will implement MNP policy on November $1^{\mathrm{s}\mathrm{t}}$, 2019 after 9 years of trials. This paper timely proposes, validates, and productizes a game theory assisted machine learning scheme to help the mobile network operator (MNO) in China make a strategy to proactively cope with their competitors in the same MNP market. The scheme further develops a set of MNP tactics for the MNO to detect user churn behaviors and to remedy the users with appropriate treatments. Experimental results demonstrate that the scheme can guide the MNOs to make targeted MNP strategy and precisely identify the “abnormal” subscribers who tend to churn out and potential new subscribers who may churn in. The scheme has been successfully implemented in production to greatly improve the marketing efficiency and user satisfaction in terms of an approximately 50% reduction of user churn for a tier-1 MNO in China.

Self-Organizing Networks (SON) is an automation technology making the planning, deployment, operation, optimization, and healing of networks simpler and faster. Legacy SON is targeted at network automation and network optimization through certain optimization rules and policies which are globally applied in networks. However, scalable and targeted optimization is not considered yet in 3GPP. Furthermore, SON is driven by performance optimization rather than ultimately improving user Quality of Experience (QoE). The impact of application characteristics on network performance and further on QoE are also not considered in 3GPP SON. This paper presents an application characteristics-driven SON system (APP-SON) to optimize 4G/5G network performance and user Quality of Experience. APP-SON leverages a scalable big data platform for targeted optimization through profiling cell application characteristics in an incremental manner in temporal space. A Hungarian Algorithm Assisted Clustering (HAAC) algorithm and a deep learning-assisted regression algorithm are developed to profile the cell application characteristics and find the targeted KPIs to be optimized for each cell. A similarity-based, parametertuning algorithm is developed to tune the corresponding engineering parameters to optimize the targeted KPIs which further improve QoE. Experimental results demonstrate that the APP-SON system can precisely profile cell traffic and application characteristics to find the targeted KPIs for optimization for each cell. APP-SON can also automatically tune the corresponding engineering parameters to improve the corresponding KPIs, ultimately improving QoE. APP-SON has been successfully implemented in production and applied in a tier-1 operator's 4G network and as a universal SON solution it will be smoothly transitioned and applied in 5G networks for this operator.