연합학습의 국방 AI 적용 방안
Federated AI for Defense Applications
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초록
본 논문에서는 최근 떠오르고 있는 데이터 공유 및 학습 방법론인연합학습(federated learning)에 대해 탐구하고, 특히 국방 분야에서의 적용가능성을 탐색한다. 연합학습은 분산된 데이터 소스로부터 학습하는 기법으로, 개별 데이터의 개인정보 보호와 동시에 효과적인 학습 결과를 도출할 수 있다는장점이 있다. 먼저, 연합학습의 기본 원리와 구조를 설명하고, 민간 부문에서의성공적인 적용 사례를 분석한다. 이를 바탕으로 국방 분야에서의 연합학습 적용방안을 제시하며, 다양한 국방 관련 데이터를 효율적으로 활용하여 AI 모델을개선할 수 있는 구체적인 전략을 모색한다. 마지막으로, 연합학습을 국방 AI에적용함으로써 얻을 수 있는 잠재적 이점과 도전 과제를 결론으로 제시한다. 이논문은 연합학습의 이론적 배경과 실질적 적용 방안을 조명함으로써, 국방분야에서의 AI 기술 발전에 기여 하고자 한다.
Abstract
This paper presents a comprehensive examination of federated learning (FL), a novel data sharing and learning approach, with a specific focus on its applicability in the defense sector. FL operates by learning from decentralized data sources, ensuring data privacy while achieving effective learning results. Initially, this research delineates the core principles and architecture of FL, followed by a review of its successful implementations in the commercial sphere. Subsequently, the paper explores the potential of FL in defense, proposing tailored strategies to leverage diverse defense-related data for augmenting AI models. Finally, the paper discusses the prospective benefits and challenges of integrating FL into defense AI systems. The goal of this research is to contribute to the enhancement of AI technologies in defense, providing insights into both the theoretical framework and practical uses of FL.
참고문헌 (25)
- Osvaldo Simeone, "Machine learning for engineers" (2022), Cambridge University Press
- Brendan McMahan, "Communication-efficient learning of deep networks from decentralized data", Proc. AISTAT, Fort Lauderdale (2017)
- SolmazNiknam, "Federated learning for wireless communications : Motivation, opportunities, and challenges", IEEE Commun. Mag, 58(6), pp.46~51 (2020)
- Latif U Khan, "Federated learning for internet of things: Recent advances, taxonomy, and open challenges", IEEE Commun. Surv. Tutor (2021)
- SolmazNiknam, "Federated learning for wireless communications : Motivation, opportunities, and challenges", IEEE Commun. Mag, 58(6), pp.46~51 (2020)
- P. Kairouz, "Found. Trends Mach. Learn" (2021), Now Publishers
- Youngjoon Lee, "Accelerated federated learning via greedy aggregation", IEEE Commun. Lett, 26(12), pp.2919~2923 (2022)
- Yue Zhao, "Federated learning with non-iid data", arXiv preprint arXiv:1806.00582, ~ (2018)
- Tian Li, "Federated learning : Challenges, methods, and future directions", IEEE Signal Process. Mag, 37(3), pp.50~60 (2020)
- Mammen, Priyanka Mary, "Federated learning: Opportunities and challenges", arXiv preprint arXiv:2101.05428, ~ (2021)
- Tian Li, "Federated optimization in heterogeneous networks", Proc. MLSys, Austin (2020)
- Lim, Wei Yang Bryan, "Federated learning in mobile edge networks: A comprehensive survey", IEEE Communications Surveys & Tutorials, 22(3), pp.2031~2063 (2020)
- Khan, Latif U, "Federated learning for internet of things: Recent advances, taxonomy, and open challenges", IEEE Communications Surveys &Tutorials, 23(3), pp.1759~1799 (2021)
- Rauniyar, Ashish, "Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions", IEEE Internet of Things Journal (2023)
- Chowdhury, Alexander, "A review of medical federated learning:Applications in oncology and cancer research", International MICCAI Brainlesion Workshop, pp.3~24 (2021), Springer International Publishing
- Yan, Rui, "Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging", IEEE Transactions on Medical Imaging (2023)
- Dash, Bibhu, "Federated learning for privacy-preserving: A review of PII data analysis in Fintech", International Journal of Software Engineering & Applications (IJSEA) (2022)
- Arora, Sunpreet, "Privacy-Preserving Financial Anomaly Detection via Federated Learning & Multi-Party Computation", arXiv preprint arXiv:2310.04546, ~ (2023)
- Long, Guodong, "Federated Learning: Privacy and Incentive", pp.240~254 (2020), Springer International Publishing
- Hard, Andrew, "Federated learning for mobile keyboard prediction", arXiv preprint arXiv:1811.03604, ~ (2018)
- Ramaswamy, Swaroop, "Federated learning for emoji prediction in a mobile keyboard", arXiv preprint arXiv:1906.04329, ~ (2019)
- Yang, Timothy, "Applied federated learning: Improving google keyboard query suggestions", arXiv preprint arXiv:1812.02903, ~ (2018)
- Qolomany, Basheer, "Particle swarm optimized federated learning for industrial IoT and smart city services", GLOBECOM 2020-2020 IEEE Global Communications Conference, pp.1~6 (2020), IEEE
- Huang, Anbu, "Starfl: Hybrid federated learning architecture for smart urban computing", ACM Transactions on Intelligent Systems and Technology (TIST), 12(4), pp.1~23 (2021)
- Sater, Raed Abdel, "A federated learning approach to anomaly detection in smart buildings", ACM Transactions on Internet of Things, 2(4), pp.1~23 (2021)