A Systematic Literature Review on Federated Learning: From A Model Quality Perspective

Abstract

As an emerging technique, Federated Learning (FL) can jointly train a global model with the data remaining locally, which effectively solves the problem of data privacy protection through the encryption mechanism. The clients train their local model, and the server aggregates models until convergence. In this process, the server uses an incentive mechanism to encourage clients to contribute high-quality and large-volume data to improve the global model. Although some works have applied FL to the Internet of Things (IoT), medicine, manufacturing, etc., the application of FL is still in its infancy, and many related issues need to be solved. Improving the quality of FL models is one of the current research hotspots and challenging tasks. This paper systematically reviews and objectively analyzes the approaches to improving the quality of FL models. We are also interested in the research and application trends of FL and the effect comparison between FL and non-FL because the practitioners usually worry that achieving privacy protection needs compromising learning quality. We use a systematic review method to analyze 147 latest articles related to FL. This review provides useful information and insights to both academia and practitioners from the industry. We investigate research questions about academic research and industrial application trends of FL, essential factors affecting the quality of FL models, and compare FL and non-FL algorithms in terms of learning quality. Based on our review's conclusion, we give some suggestions for improving the FL model quality. Finally, we propose an FL application framework for practitioners.

References in corpus (43)

• Federated Learning for Mobile Keyboard Prediction
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
• Secure Federated Transfer Learning
• Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation
• Differentially Private Federated Learning: A Client Level Perspective
• Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications
• Applied Federated Learning: Improving Google Keyboard Query Suggestions
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
• Federated Meta-Learning with Fast Convergence and Efficient Communication
• Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
• Protection Against Reconstruction and Its Applications in Private Federated Learning
• Threats to Federated Learning: A Survey
• MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets
• BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
• Inverting Gradients -- How easy is it to break privacy in federated learning?
• SecureBoost: A Lossless Federated Learning Framework
• Differential Privacy-enabled Federated Learning for Sensitive Health Data
• Bayesian Nonparametric Federated Learning of Neural Networks
• Variational Federated Multi-Task Learning
• Federated Learning Of Out-Of-Vocabulary Words
• Federated Learning with Matched Averaging
• Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
• PrivFL: Practical Privacy-preserving Federated Regressions on High-dimensional Data over Mobile Networks
• Real-World Image Datasets for Federated Learning
• FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
• HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
• A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
• Federated and Differentially Private Learning for Electronic Health Records
• FOCUS: Dealing with Label Quality Disparity in Federated Learning
• Federated User Representation Learning
• A Federated Learning Approach for Mobile Packet Classification
• Towards Federated Graph Learning for Collaborative Financial Crimes Detection
• Federated Residual Learning
• Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks
• An On-Device Federated Learning Approach for Cooperative Model Update between Edge Devices
• Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
• Combining Federated and Active Learning for Communication-efficient Distributed Failure Prediction in Aeronautics
• Quantification of the Leakage in Federated Learning
• Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks
• Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method
• Privacy-preserving Weighted Federated Learning within Oracle-Aided MPC Framework
• Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records

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