Accountable Federated Machine Learning in Government: Engineering and Management Insights

Balta, Dian; Sellami, Mahdi; Kuhn, Peter; Schöpp, Ulrich; Buchinger, Matthias; Baracaldo, Nathalie; Anwar, Ali; Ludwig, Heiko; Sinn, Mathieu; Purcell, Mark; Altakrouri, Bashar

doi:10.1007/978-3-030-82824-0_10

Cited by 9 publications

(11 citation statements)

References 35 publications

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“…This could be the case due to technological challenges solely being solved in the literature but not in real-world scenarios. Third, FL can evolve into a business model (Yang et al, 2019;Balta et al, 2021), which gives entities an incentive to take part in intergovernmental projects. This mitigates disincentives in data sharing caused by "free riding" and the problem of inefficiency due to collective action (Olson, 1965).…”

Section: Discussionmentioning

confidence: 99%

“…Yang et al (2019) estimate that FL will evolve into a business model where participants in an FL project can profit from the value they contribute to the model. FL thus allows participants to pursue joint business activities (Balta et al, 2021). An example of such joint business activity is given by Manoj et al (2022), training a model for predicting the yield of crops.…”

Section: Challenge 3: Misaligned Incentives and Limitations Of Curren...mentioning

confidence: 99%

“…There are first approaches to frameworks on how to apply FL in eGOV3.0 use cases, which include the specification of client, server, model, and application programming interface requirements at the start of a project (Guberovic et al, 2022). Further, the accountability of FL in government to overcome legislative constraints has been researched (Balta et al, 2021). Balta et al (2021) both the engineering side, focusing on system design, and the management side, underscoring the crucial role of trust-building among the participating entities.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Further, the accountability of FL in government to overcome legislative constraints has been researched (Balta et al, 2021). Balta et al (2021) both the engineering side, focusing on system design, and the management side, underscoring the crucial role of trust-building among the participating entities.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

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Overcoming intergovernmental data sharing challenges with federated learning

Sprenkamp,

Delgado Fernández,

Eckhardt

et al. 2024

Data & Policy

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Intergovernmental collaboration is needed to address global problems. Modern solutions to these problems often include data-driven methods like artificial intelligence (AI), which require large amounts of data to perform well. As AI emerges as a central catalyst in deriving effective solutions for global problems, the infrastructure that supports its data needs becomes crucial. However, data sharing between governments is often constrained due to socio-technical barriers such as concerns over data privacy, data sovereignty issues, and the risks of information misuse. Federated learning (FL) presents a promising solution as a decentralized AI methodology, enabling the use of data from multiple silos without necessitating central aggregation. Instead of sharing raw data, governments can build their own models and just share the model parameters with a central server aggregating all parameters, resulting in a superior overall model. By conducting a structured literature review, we show how major intergovernmental data-sharing challenges listed by the Organisation for Economic Co-operation and Development can be overcome by utilizing FL. Furthermore, we provide a tangible resource implementing FL linked to the Ukrainian refugee crisis that can be utilized by researchers and policymakers alike who want to implement FL in cases where data cannot be shared. Enhanced AI while maintaining privacy through FL thus allows governments to collaboratively address global problems, positively impacting governments and citizens.

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Section: Discussionmentioning

confidence: 99%

Section: Challenge 3: Misaligned Incentives and Limitations Of Curren...mentioning

confidence: 99%

Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Related Work and Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Overcoming intergovernmental data sharing challenges with federated learning

Sprenkamp,

Delgado Fernández,

Eckhardt

et al. 2024

Data & Policy

View full text Add to dashboard Cite

show abstract

“…In addition, there can be difficulties of gaining regulatory approval of accessing data (for instance in healthcare), or even lack of data (geographical data) in order for an ML system to be properly trained for quality results. One of the challenges in producing e-Government services built on FL is in ensuring fairness and reproducibility, which is well emphasized in a paper on an analysis framework suitable for governmental scenarios in FL applications [35].…”

Section: Related Work Third Generation E-governmentmentioning

confidence: 99%

Framework for Federated Learning Open Models in e-Government Applications

Guberović¹,

Alexopoulos²,

Bosnić³

et al. 2022

Interdisciplinary Description of Complex Systems

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Privacy preserving and secure robust federated learning: A survey

Han,

Lu,

Wang

et al. 2024

Concurrency and Computation

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SummaryFederated learning (FL) has emerged as a promising solution to address the challenges posed by data silos and the need for global data fusion. It offers a distributed machine learning framework with privacy‐preserving features, allowing model training without the need to collect user data. However, FL also presents significant security and privacy threats that hinder its widespread adoption. The requirements of privacy and security in FL are inherently conflicting. Privacy necessitates the concealment of individual client updates, while security requires the disclosure of client updates to detect anomalies. While most existing research focused on the privacy and security aspects of FL, very few studies have addressed the compatibility of these two demands. In this work, we aim to bridge this gap by proposing a comprehensive defense scheme that ensures privacy, security, and compatibility in FL. We categorize the existing literature into two key directions: privacy defense and security defense. Privacy defense includes methods based on additive masks, differential privacy, homomorphic encryption, and trusted execution environment, whereas security defense encompasses distance‐, performance‐, clustering‐, and similarity‐based anomaly detection techniques and statistical information‐based anomaly update bypassing techniques when the server is trusted and privacy‐compatible anomaly update detection techniques when the server is not trusted. In addition, this article presents decentralized FL solutions based on blockchain. For each direction, we discuss specific technical solutions, their advantages, and disadvantages. By evaluating various defense methods, we identify the most suitable approach to address the primary challenge of “achieving a secure and robust FL system against malicious adversaries while protecting users' privacy.” We then propose a theoretical reference framework for end‐to‐end protection of privacy and security in FL for the key problem, which summarizes the attack surface of FL systems from the client to the server under the security model where the client and server are malicious. Leveraging the strengths and characteristics of existing schemes, our proposed framework integrates multiple techniques to strike a balance between privacy, usability, and efficiency. This framework serves as a valuable reference and provides insights for future work in the field. Finally, we also provide recommendations for future research directions in this field.

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Accountable Federated Machine Learning in Government: Engineering and Management Insights

Cited by 9 publications

References 35 publications

Overcoming intergovernmental data sharing challenges with federated learning

Overcoming intergovernmental data sharing challenges with federated learning

Framework for Federated Learning Open Models in e-Government Applications

Privacy preserving and secure robust federated learning: A survey

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