The Applicability of Federated Learning to Official Statistics

• URL: http://arxiv.org/abs/2307.15503v2
• Date: Fri, 29 Sep 2023 11:02:12 GMT
• Title: The Applicability of Federated Learning to Official Statistics
• Authors: Joshua Stock, Oliver Hauke, Julius Wei{\ss}mann, Hannes Federrath
• Abstract summary: This work investigates the potential of Federated Learning for official statistics. It shows how well the performance of FL models can keep up with centralized learning methods.
• Score: 0.5461938536945721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work investigates the potential of Federated Learning (FL) for official statistics and shows how well the performance of FL models can keep up with centralized learning methods.F L is particularly interesting for official statistics because its utilization can safeguard the privacy of data holders, thus facilitating access to a broader range of data. By simulating three different use cases, important insights on the applicability of the technology are gained. The use cases are based on a medical insurance data set, a fine dust pollution data set and a mobile radio coverage data set - all of which are from domains close to official statistics. We provide a detailed analysis of the results, including a comparison of centralized and FL algorithm performances for each simulation. In all three use cases, we were able to train models via FL which reach a performance very close to the centralized model benchmarks. Our key observations and their implications for transferring the simulations into practice are summarized. We arrive at the conclusion that FL has the potential to emerge as a pivotal technology in future use cases of official statistics.

Related papers

• Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? [50.03434441234569]
  Federated Learning (FL) has gained significant popularity due to its effectiveness in training machine learning models across diverse sites without requiring direct data sharing. While various algorithms have shown that FL with local updates is a communication-efficient distributed learning framework, the generalization performance of FL with local updates has received comparatively less attention.
  arXiv  Detail & Related papers  (2024-09-05T19:00:18Z)
• StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems [22.259297167311964]
  Federated learning (FL) is a decentralized learning technique that enables devices to collaboratively build a shared Machine Leaning (ML) or Deep Learning (DL) model without revealing their raw data to a third party. Due to its privacy-preserving nature, FL has sparked widespread attention for building Intrusion Detection Systems (IDS) within the realm of cybersecurity. We propose an effective method called Statistical Averaging (StatAvg) to alleviate non-independently and identically (non-iid) distributed features across local clients' data in FL.
  arXiv  Detail & Related papers  (2024-05-20T14:41:59Z)
• FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units [50.930389484343515]
  Roadside unit (RSU) can significantly improve the safety and robustness of autonomous vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a single RSU mainly focuses on real-time inference and V2X collaboration. Integrating the vast amounts of data from numerous RSUs can provide a rich source of data for model training. We introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation.
  arXiv  Detail & Related papers  (2024-01-23T15:52:57Z)
• Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches [37.192249479129444]
  Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering community, the statistical field has also explored similar privacy-preserving algorithms. We present the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
  arXiv  Detail & Related papers  (2023-11-06T10:11:59Z)
• Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation [17.096806029281385]
  Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. We develop a vicinal feature-level data augmentation scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation.
  arXiv  Detail & Related papers  (2023-10-23T21:14:52Z)
• StatMix: Data augmentation method that relies on image statistics in federated learning [0.27528170226206433]
  StatMix is an augmentation approach that uses image statistics to improve results of FL scenario(s) In all FL experiments, application of StatMix improves the average accuracy, compared to the baseline training (with no use of StatMix)
  arXiv  Detail & Related papers  (2022-07-08T19:02:41Z)
• Do Gradient Inversion Attacks Make Federated Learning Unsafe? [70.0231254112197]
  Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. Recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in real FL use-cases and provide a new baseline attack.
  arXiv  Detail & Related papers  (2022-02-14T18:33:12Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• Prototype Guided Federated Learning of Visual Feature Representations [15.021124010665194]
  Federated Learning (FL) is a framework which enables distributed model training using a large corpus of decentralized training data. Existing methods aggregate models disregarding their internal representations, which are crucial for training models in vision tasks. We introduce FedProto, which computes client deviations using margins of representations learned on distributed data.
  arXiv  Detail & Related papers  (2021-05-19T08:29:12Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)
• A Principled Approach to Data Valuation for Federated Learning [73.19984041333599]
  Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. This paper proposes a variant of the SV amenable to FL, which we call the federated Shapley value.
  arXiv  Detail & Related papers  (2020-09-14T04:37:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.