FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation
Not on the map. Why?
Abstract (arXiv)
Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. The learning scheme may be horizontal, vertical or hybrid (both vertical and horizontal). Most existing research work with deep neural network (DNN) modelling is focused on horizontal data distributions, while vertical and hybrid schemes are much less studied. In this paper, we propose a generalized algorithm FedEmb, for modelling vertical and hybrid DNN-based learning. The idea of our algorithm is characterised by higher inference accuracy, stronger privacy-preserving properties, and lower client-server communication bandwidth demands as compared with existing work. The experimental results show that FedEmb is an effective method to tackle both split feature & subject space decentralized problems, shows 0.3% to 4.2% inference accuracy improvement with limited privacy revealing for datasets stored in local clients, and reduces 88.9 % time complexity over vertical baseline method.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 64 references that are in the map. First posted on arXiv 30 Nov 2023.
Builds on
24 of 64 references.
References · 24
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2018Federated Optimization in Heterogeneous Networkscited by 2,987
- 2016Deep Learning with Differential Privacycited by 2,728
- 2019Deep Leakage from Gradientscited by 1,021
- 2018Gradient Descent Provably Optimizes Over-parameterized Neural Networkscited by 659
- 2020Federated Learning with Matched Averagingcited by 564
- 2018Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layerscited by 340
- 2018Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Datacited by 261
- 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
- 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 156
- 2021Decentralized Federated Learning through Proxy Model Sharingcited by 57
- 2021FLOP: Federated Learning on Medical Datasets using Partial Networkscited by 42
12 more
- 2020Hybrid Differentially Private Federated Learning on Vertically Partitioned Datacited by 22
- 2019Secure and Efficient Federated Transfer Learningcited by 21
- 2022One-shot Federated Learning without Server-side Trainingcited by 20
- 2020Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learningcited by 19
- 2014Protecting Locations with Differential Privacy under Temporal Correlationscited by 17
- 2023FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learningcited by 15
- 2019Learning Privately over Distributed Features: An ADMM Sharing Approachcited by 14
- 2021Efficient and Private Federated Learning with Partially Trainable Networkscited by 13
- 2015Differentially Private Distributed Convex Optimization via Functional Perturbationcited by 11
- 2015On the Generalization Properties of Differential Privacycited by 5
- 2021Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-levelcited by 5
- 2023Sentiment analysis with adaptive multi-head attention in Transformercited by 5
Built on by
Citing papers per year: 2024: 2.
2 papers.