With the rise of cloud-edge collaboration, recommendation services are increasingly trained in distributed environments. Federated Recommendation (FR) enables such multi-end collaborative training while preserving privacy by sharing model parameters instead of raw data. However, the large number of parameters, primarily due to the massive item embeddings, significantly hampers communication efficiency. While existing studies mainly focus on improving the efficiency of FR models, they largely overlook the issue of embedding parameter overhead. To address this gap, we propose a FR training framework with Parameter-Efficient Fine-Tuning (PEFT) based embedding designed to reduce the volume of embedding parameters that need to be transmitted. Our approach offers a lightweight, plugin-style solution that can be seamlessly integrated into existing FR methods. In addition to incorporating common PEFT techniques such as LoRA and Hash-based encoding, we explore the use of Residual Quantized Variational Autoencoders (RQ-VAE) as a novel PEFT strategy within our framework. Extensive experiments across various FR model backbones and datasets demonstrate that our framework significantly reduces communication overhead while improving accuracy.

Personal Knowledge Graphs (PKGs) organize an individual user's information into a structured format comprising entities, attributes, and relationships. By leveraging this structured and semantically rich data, PKGs have become essential for securing personal data management and delivering personalized services. To unlock their potential in personalized recommendations, prior research has explored the construction of PKGs and recommendation methods built upon them. However, these studies often overlook challenges associated with distributed PKGs across different users, such as joint training and privacy protection. To address these challenges, we propose PKGRec, a federated graph recommendation method specifically designed for PKGs, which utilizes a federated learning framework to ensure user privacy and data security during joint learning. Furthermore, to accommodate the user-centric graph structure of PKGs, our approach categorizes entities into three types: users, items, and other entities. It then applies a novel staged graph convolution method to model various entities based on these entity categories during local training. To enable efficient graph information sharing among distributed PKGs without requiring additional data transfer or aggregation, PKGRec performs graph expansion on the trained gradients by federated aggregation. Extensive experiments conducted on four publicly available datasets demonstrate that our method consistently outperforms the existing federated recommendation approaches.

With the rapid growth of distributed learning and workflow orchestration, Federated Edge Learning has emerged as a solution, enabling multiple edge devices to collaboratively train a large model without the need for sharing raw data. Beyond considering bandwidth and computational resource limitations in the Internet of Things (IoT) environment, it is crucial to address the issue of IoT devices often collecting data that lacks timely annotations, which can lead to latency and label deficiency issues. In most Federated Edge Learning mechanisms, clients’ weights are selected for offloading to the server. In this paper, we propose a solution for dynamic edge selection and wireless network allocation under semi-supervised and privacy protection settings, termed Semi-supervised Scheduling and Allocation Optimization for Federated Edge Learning (SSAFL). SSAFL is designed to adapt to various scenarios, including channel state variations, device heterogeneity, resource incentives, deadline control, label deficiencies, and Non-IID data distributions. This adaptability is achieved through the utilization of an Incentive Optimization framework that encompasses bandwidth allocation and device scheduling policies. Within SSAFL, we introduce the concept of a weighted bipartite graph network to tackle the Incentive Optimization problem and achieve a balance in large-scale optimization of device selection. Additionally, to address the label deficiency issue, we devise a Dynamic Timer for deadline control for each client. Comprehensive and confidential results demonstrate that our proposed approach significantly outperforms other Federated Edge Learning baselines.

Data ecosystems (DE) are the future directions of data management and play a vital role in unlocking the value of data. Service Recommender Systems (RS) are typical applications in DEs. For example, deep learning-based RS on the basis of extensive data from various fields can help organizations obtain valuable insights of data. As organizations from different fields share data for better recommendation services, the risk of privacy leakage which is harmful to DEs increases. Due to privacy concerns, Vertical Federated Learning (VFL), a privacy-preserving computing technology for joint learning and privacy recommendation models among organizations in various fields, has garnered significant attention. Existing VFL methods are training models with static data from specific fields when there are new recommendation scenarios or fields. In addition, the models are fixed after one training session. Therefore, these models can only be applied to several specific recommendation fields and they can’t utilize continuously generated data that corresponds to various fields, posing challenges for long-term and extensive cooperation. To tackle these challenges, we introduce Vertical Federated Continual Learning (VFCL), which extends Continual Learning (CL) into the VFL framework to enable VFL models to sustainably adapt to new scenarios. We discuss feasible solutions based on existing CL methods. Furthermore, we propose GCPN, a method based on a dynamic architecture in VFCL. GCPN introduces fewer parameters for each new field by utilizing group connected layers and scale layers, eliminating the need for storing or using past data. It effectively alleviates the problem of catastrophic forgetting, a major issue in CL, while preserving privacy in joint recommendations. To evaluate GCPN, we construct the VFCL scenario using Amazon's public recommendation datasets. Experiments demonstrate that our method enhances the effectiveness of most tasks in CL and VFCL scenarios.

Personal data cloud, as an emerging personal data management mode in recent years, enables to reduce the risk of privacy disclosure and protect the rights and interests of individuals given by privacy protection laws and regulations. Personal data that is generated during the interaction between individual users and various services contains a lot of useful personalized but private information and plays a crucial part in personalized service recommendation. In traditional service recommendation scenario, personal data of massive users is centralized owned/managed by service providers, which is easy to lead to privacy disclosure and personal data abuse. In the personal data cloud based service recommendation scenario, personal data of individual users is distributed stored and controlled by users themselves. To address the challenges of privacy protection and distributed storage of personalized data in this new recommendation scenario, we propose HyFL, a deep learning based recommendation algorithm with hybrid federated learning. HyFL can conduct recommendation based on the personal data from multiple services. The security of HyFL is theoretically proved, and experiments on real-world datasets demonstrate that HyFL performs better on the basis of privacy preservation than that of some traditional recommendation approaches.

Privacy preservation is drawing growing concern. Due to personal data of massive users is centralizedly owned/managed by service providers at present, it is very easy to lead to privacy disclosure and personal data abuse, therefore many countries and regions put forward privacy protection laws and regulations and researchers also propose some de-centralized personal data management systems. Nevertheless, the implementation of decentralized data management model means a radical change in current service development and service model. For service providers, it is too difficult and costly to realize these personal data management systems, and there are also no additional economic benefits. These personal data management systems are too ideal to popularize from a business model perspective, so the development of decentralized models proceeds slowly. In response to this situation, we propose a new personal data management service which realizes the transition stage between reality and ideal where users’ data can exist both in users’ personal data cloud and service providers. To address the challenges of data collection and data semantic uniformity caused by personal data cloud at this transition stage, we propose a personal data management architecture based on SoLiD (Social Linked Data) and DTP (Data Transfer Project). The proposed architecture could transfer users’ data from service providers to personal data cloud and fuse heterogeneous data through data fusion technology so as to provide users with the ability to manage their own data and a unified view of data. The feasibility and efficiency of the proposed service are practically proved through conducting a case study.

The distributed services from different domains, organizations, and regions in the real and virtual world are converged together to form the Internet of Services (IoS). It is required to provide a trusted, orderly and efficient platform environment for service collaboration and delivery. Blockchain is currently recognized as the best practice technique to solve the problem of orderly and trusted execution of system. However, the efficiency issue introduced by it is also a problem that every platform needs to take seriously. Therefore, this paper proposes a blockchain-based IoS architecture, which uses the virtual chain and the embedded EVM(Ethereum Virtual Machine) notary technology to ensure the security and credibility of the service collaboration process in the IoS, and also the efficient operation of the entire system. This architecture has been compared with non-blockchain architecture without credible guarantees and also blockchain-only architecture. This comparative experiment shows that the proposed architecture not only ensures the orderly and trusted execution of the system, but also effectively controls the loss of performance.