Framework for Federated Learning Open Models in e-Government Applications

Guberović, Emanuel; Alexopoulos, Charalampos; Bosnić, Ivana; Čavrak, Igor

doi:10.7906/indecs.20.2.8

Cited by 8 publications

(5 citation statements)

References 30 publications

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“…However, research in the area of eGOV3.0 is limited. While Guberovic et al (2022) created a framework specifying component requirements for government FL projects and Balta et al (2021) analyzed the accountability of FL in government, we analyzed how FL can solve the problem of intergovernmental data sharing. We did so by conducting a structured literature review, which served the purpose of analyzing how FL can solve challenges identified by OECD (2019).…”

Section: Discussionmentioning

confidence: 99%

“…FL thus provides incentives in terms of privacy, security, legal, and economic benefits for users (Yang et al, 2019). 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).…”

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: Related Work and Backgroundmentioning

confidence: 99%

Overcoming intergovernmental data sharing challenges with federated learning

Sprenkamp,

Delgado Fernández,

Eckhardt

et al. 2024

Data & Policy

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show abstract

“…However, research in the area of eGOV3.0 is limited. While Guberović et al (2022) created a framework specifying component requirements for government FL projects and Balta et al (2021) analysed the accountability of FL in government, we analysed how FL can solve the problem of intergovernmental data sharing. We did so by conducting a structured literature review, which served the purpose of analysing how FL can solve challenges identified by OECD (2019).…”

Section: Discussionmentioning

confidence: 99%

“…Yang et al, 2019). To the extent of our knowledge, one framework on how to apply FL in eGOV3.0 use cases (Guberović et al, 2022) has been created, which includes the specification of client, server, model and application programming interface requirements at the start of a project. Further, the accountability of FL in government to overcome legislative constraints has been researched, pointing out engineering requirements, i.e., architecture design and management requirements, i.e., trust among actors (Balta et al, 2021).…”

Section: Federated Learningmentioning

confidence: 99%

Reviving Simulated Annealing: Lifting its Degeneracies for Real-Time Job Scheduling

Schmidt,

Köhler,

Borstell

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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To address global problems, intergovernmental collaboration is needed.Modern solutions to these problems often include data-driven methods like artificial intelligence (AI), which require large amounts of data to perform well. However, data sharing between governments is limited. A possible solution is federated learning (FL), a decentralised AI method created to utilise personal information on edge devices. Instead of sharing data, governments can build their own models and just share the model parameters with a centralised server aggregating all parameters, resulting in a superior overall model. By conducting a structured literature review, we show how major intergovernmental data sharing challenges like disincentives, legal and ethical issues as well as technical constraints can be solved through FL. Enhanced AI while maintaining privacy through FL thus allows governments to collaboratively address global problems, which will positively impact governments and citizens.

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“…The necessity for inter‐governmental information sharing leverages the decentralized nature of FL, simultaneously safeguarding the privacy of sensitive data. The application of e‐government is proposed in Reference 161, and an analytical framework called accountable federated machine learning is introduced in Reference 162 to address management challenges within government. In Reference 163, challenges and solutions for implementing FL to facilitate data sharing among multiple governments are summarized.…”

Section: Introduction To Federated Learningmentioning

confidence: 99%

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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Framework for Federated Learning Open Models in e-Government Applications

Cited by 8 publications

References 30 publications

Overcoming intergovernmental data sharing challenges with federated learning

Overcoming intergovernmental data sharing challenges with federated learning

Reviving Simulated Annealing: Lifting its Degeneracies for Real-Time Job Scheduling

Privacy preserving and secure robust federated learning: A survey

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