Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks

2024-03-05 · Yaqian Qi, Yuan Feng, Xiangxiang Wang, Hanzhe Li, Jingxiao Tian

To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting machine learning paradigm known as Federated Learning (FL), which enables data owners to train models without having to transfer raw data to third-party servers. However, FL networks are expected to involve thousands of heterogeneous distributed devices. As a result, communication efficiency remains a key bottleneck. To reduce node failures and device exits, a Hierarchical Federated Learning (HFL) framework is proposed, where a designated cluster leader supports the data owner through intermediate model aggregation. Therefore, based on the improvement of edge server resource utilization, this paper can effectively make up for the limitation of cache capacity. In order to mitigate the impact of soft clicks on the quality of user experience (QoE), the authors model the user QoE as a comprehensive system cost. To solve the formulaic problem, the authors propose a decentralized caching algorithm with federated deep reinforcement learning (DRL) and federated learning (FL), where multiple agents learn and make decisions independently

📄 PDF Abstract BibTeX arXiv:2403.03165

Code (0)

등록된 구현이 없습니다.

Tasks

Cloud ComputingDeep Reinforcement LearningEdge-computingFederated Learning Recommendation Systems

P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network

2024-10-16 · Zheng Wang, Wanwan Wang, Yimin Huang, Zhaopeng Peng 외

In recent years, graph neural networks (GNNs) have been commonly utilized for social recommendation systems. However, real-world scenarios often present challenges related to user privacy and business constraints, inhibi…

Privacy Preserving Recommendation Systems

UFGraphFR: An attempt at a federated recommendation system based on user text characteristics

2025-01-14 · Xudong Wang

Federated learning has emerged as a key paradigm in privacy-preserving computing due to its "data usable but not visible" property, enabling users to collaboratively train models without sharing raw data. Motivated by th…

Attribute Federated Learning graph construction Privacy Preserving+1

FedeRank: User Controlled Feedback with Federated Recommender Systems

2020-12-15 · Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara 외

Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent concerns in the digital era. After several …

Diversity Federated Learning Privacy Preserving Recommendation Systems

When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions

2023-05-22 · Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang 외

Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recommendation model and users' private data …

Attribute Federated Learning Recommendation Systems

Federated Recommendation via Hybrid Retrieval Augmented Generation

2024-03-07 · Huimin Zeng, Zhenrui Yue, Qian Jiang, Dong Wang

Federated Recommendation (FR) emerges as a novel paradigm that enables privacy-preserving recommendations. However, traditional FR systems usually represent users/items with discrete identities (IDs), suffering from perf…

Hallucination Privacy Preserving RAG Re-Ranking+2