Federated Learning over Harmonized Data Silos
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Abstract (arXiv)
Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sharing data. Most of the existing work operates on unstructured data, such as images or text, or on structured data assumed to be consistent across the different sites. However, sites often have different schemata, data formats, data values, and access patterns. The field of data integration has developed many methods to address these challenges, including techniques for data exchange and query rewriting using declarative schema mappings, and for entity linkage. Therefore, we propose an architectural vision for an end-to-end Federated Learning and Integration system, incorporating the critical steps of data harmonization and data imputation, to spur further research on the intersection of data management information systems and machine learning.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 59 references that are in the map. First posted on arXiv 15 May 2023.
Builds on
28 of 59 references.
References · 28
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2016Deep Learning with Differential Privacycited by 2,682
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
- 2020Adaptive Federated Optimizationcited by 1,036
- 2019Towards Federated Learning at Scale: System Designcited by 826
- 2018LEAF: A Benchmark for Federated Settingscited by 780
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- 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
16 more
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
- 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 349
- 2021A Field Guide to Federated Optimizationcited by 312
- 2019Asynchronous Federated Optimizationcited by 264
- 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
- 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
- 2020FedVision: An Online Visual Object Detection Platform Powered by Federated Learningcited by 121
- 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
- 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 51
- 2021Privacy-preserving Federated Learning based on Multi-key Homomorphic Encryptioncited by 46
- 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
- 2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 19
- 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
- 2021Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settingscited by 11
- 2021Scaling Neuroscience Research using Federated Learningcited by 10
- 2020Accelerating Federated Learning in Heterogeneous Data and Computational Environmentscited by 6
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