Federated meta-learning for recommendation

Author: euxy

August undefined, 2024

WebFeb 21, 2024 · PDF Recommender systems have been widely studied from the machine learning perspective, where it is crucial to share information among users while … WebJan 25, 2024 · FedFast puts forward an accelerated strategy of federated learning for recommendation. Because the traditional federated learning algorithm converges slowly for recommendation, it will continue to occupy the equipment resources of the client during model training. ... this paper proposes a federated recommendation algorithm based …

MetaSelector: Meta-Learning for Recommendation with …

WebFeb 22, 2024 · Recommender systems have been widely studied from the machine learning perspective, where it is crucial to share information among users while preserving user … WebFederated Meta-Learning with Fast Convergence and Efficient Communication. Statistical and systematic challenges in collaboratively training machine learning models across … myriam b thiele md

Federated Meta-Learning: Democratizing Algorithm Selection Across ...

WebFederated Meta-Learning with Fast Convergence and Efficient Communication Fei Chen*, Mi Luo*, Zhenhua Dong, Zhenguo Li and Xiuqiang He Link: arXiv Preliminary version: … WebSep 19, 2024 · Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN. ... Federated Social Recommendation with Graph Neural Network paper ... Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting. paper code; WebJan 25, 2024 · Federated learning is a distributed machine learning framework that can be applied in recommendation systems to solve privacy protection issues. It saves users’ … the sole one

FedFast: Going Beyond Average for Faster Training of Federated ...

Meta Matrix Factorization for Federated Rating Predictions

WebJul 19, 2024 · The performance of the three federated learning-based baselines is not very different, and the top-performing method FedFast achieves competitive results with the … WebWelcome to IJCAI IJCAI myriam bachirWebApr 14, 2024 · Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers. ... Junshu He, and Bingran Zuo. 2024. "ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection" Mathematics 11, no. 8: 1867. … myriam baeckeroot

"WebDevice-cloud Collaborative Recommendation via Meta Controller. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4353–4362. ... FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server. arXiv preprint arXiv:2204.11536 (2024). " - Federated meta-learning for recommendation

Federated meta-learning for recommendation

FLOP: Federated Learning on Medical Datasets using Partial Networks ...

WebOct 3, 2024 · 1.3 Contributions. We highlight our contributions below: We develop a privacy-preserving recommendation model called PrivRec based on FL. Apart from preventing users from sharing their own data for model training, we propose an efficient and practical meta-learning approach to enable PrivRec to quickly adapt to inactive users, alleviating … WebApr 14, 2024 · 3.1 Recommender Systems. Neural Collaborative Filtering (NCF) [] is one of the most widely used deep learning based recommender models and has state-of-the-art recommendation performance.Without loss of generality, we adopt NCF as our base recommender model. Respectively, let M and N denote the number of users and items in …

Did you know?

WebFeb 22, 2024 · Recommender systems have been widely studied from the machine learning perspective, where it is crucial to share information … WebAlways learning as it is a Day One. I am motivated by challenging problems that lead to broad user impact. As much as I enjoy solving user problems, I am equally passionate about building happy ...

Webimplementation of federated learning techniques in practice as user devices often have limited network bandwidth and computation resource to operate recommendation … WebKeywords: Meta learning · Cross-domain recommendation · Federated learning · Cold-start · Embedding mapping 1 Introduction Recommender systems have played an important role in various online applica-tions of the Internet, which help users discover interesting content from massive Supported by organization nudt.

WebJul 25, 2024 · Federated Meta-learning for Recommendation. arXiv preprint arXiv:1802.07876 (2024). Google Scholar; Junkun Chen, Xipeng Qiu, Pengfei Liu, and Xuanjing Huang. 2024b. Meta Multi-task Learning for Sequence Modeling. In AAAI. Google Scholar; Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, and Mohan … WebFeb 21, 2024 · In this work, we present a federated meta-learning framework for recommendation in which user information is shared at the level of algorithm, instead of model or data adopted in previous...

WebRecently, [9] proposed a federated framework that integrates the aforementioned MAML for recommendation, in which a parameterized meta-algorithm is used to train the recommendation models, and ...

WebApr 30, 2024 · took advantage of meta-learning-based algorithms for federated recommendations. More recently, Tan et al . [ 35 ] proposed a federated recommender system for online services that trains a recommendation model on data from multiple parties without revealing the private information of each party. the sole of christmasWebFeb 22, 2024 · Federated Meta-Learning with Fast Convergence and Efficient Communication. Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning. In this work, we show that meta … the sole of the foot is supported by theWebJul 19, 2024 · 2.2 FMLRec Framework. We now introduce the framework of our FMLRec method for privacy-preserving recommendation. Overall, it consists of an external framework based on federated learning and a training and parameter updating approach based on MAML, as shown in Fig. 1.Following the FedAvg algorithm in [], FMLRec also … the sole of the foot rests flat on the floor