SoK: Assessing the State of Applied Federated Machine Learning
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Abstract (arXiv)
Machine Learning (ML) has shown significant potential in various applications; however, its adoption in privacy-critical domains has been limited due to concerns about data privacy. A promising solution to this issue is Federated Machine Learning (FedML), a model-to-data approach that prioritizes data privacy. By enabling ML algorithms to be applied directly to distributed data sources without sharing raw data, FedML offers enhanced privacy protections, making it suitable for privacy-critical environments. Despite its theoretical benefits, FedML has not seen widespread practical implementation. This study aims to explore the current state of applied FedML and identify the challenges hindering its practical adoption. Through a comprehensive systematic literature review, we assess 74 relevant papers to analyze the real-world applicability of FedML. Our analysis focuses on the characteristics and emerging trends of FedML implementations, as well as the motivational drivers and application domains. We also discuss the encountered challenges in integrating FedML into real-life settings. By shedding light on the existing landscape and potential obstacles, this research contributes to the further development and implementation of FedML in privacy-critical scenarios.
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References · 18
- 2019Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)cited by 657
- 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
- 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
- 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
- 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
- 2020Privacy-preserving Traffic Flow Prediction: A Federated Learning Approachcited by 82
- 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
- 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
- 2020Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applicationscited by 58
- 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
- 2022Electrical Load Forecasting Using Edge Computing and Federated Learningcited by 34
- 2021A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparisoncited by 31
6 more
- 2021Multi-task Federated Learning for Heterogeneous Pancreas Segmentationcited by 19
- 2021Real-time End-to-End Federated Learning: An Automotive Case Studycited by 15
- 2020Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learningcited by 11
- 2021FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computingcited by 11
- 2019A Distributed Fair Machine Learning Framework with Private Demographic Data Protectioncited by 8
- 2021Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classificationcited by 6
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