Toward Sustainable Environmental Intelligence: A Comprehensive Survey of Federated Learning Applications and Technical Challenges
持続可能な環境インテリジェンスに向けて：連合学習の応用と技術的課題に関する包括的サーベイ (AI 翻訳)
S. Yarham, Mehran Behjati, Haider A. H. Alobaidy
🤖 gxceed AI 要約
日本語
本論文は、環境モニタリングにおける連合学習（FL）の応用を包括的にレビューし、大気質予測、太陽エネルギー予測、カーボンフットプリント分析など多岐にわたる分野をカバーする。プライバシー保護と分散学習の利点を強調しつつ、通信オーバーヘッドや非IIDデータなどの課題を指摘し、今後の研究方向を示す。
English
This paper provides a comprehensive survey of federated learning (FL) applications in environmental monitoring, covering air quality forecasting, solar energy prediction, carbon footprint analysis, and more. It highlights privacy-preserving benefits and identifies key challenges such as communication overhead and non-IID data, proposing future research directions like energy-aware protocols and real-world testbeds.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、環境モニタリングの分散化とプライバシー保護が重要な課題であり、本サーベイはFL技術の適用可能性を整理している。特に、省エネやカーボンフットプリント分析はGX政策と親和性が高い。
In the global GX context
This survey provides a systematic overview of FL for environmental monitoring, which aligns with global trends in privacy-preserving AI for sustainability. It addresses challenges relevant to real-world deployment, such as communication efficiency and sensor reliability, offering a foundation for future research in smart environmental systems.
👥 読者別の含意
🔬研究者:This survey offers a structured overview of FL methodologies and challenges in environmental monitoring, serving as a reference for future research directions.
🏛政策担当者:Policymakers interested in data privacy in environmental monitoring can gain insights into FL's potential and limitations from this survey.
📄 Abstract（原文）
Environmentalmonitoring systems increasingly face the challenge of processing vast volumes of distributed, heterogeneous, and sensitive data in real time. Federated Learning (FL), a decentralized machine learning paradigm, offers a compelling solution by enabling collaborative model training across edge devices while preserving data privacy. This paper presents a comprehensive review of recent advancements in FL-driven environmental monitoring across domains such as air quality forecasting, traffic flow optimization, solar energy prediction, water quality assessment, carbon footprint analysis, and disaster response. We analyze how state-of-the-art FL methodologies, including diverse machine and deep learning models, hierarchical and multi-task architectures, semantic communication, and privacy-preserving aggregation, enhance prediction accuracy, communication efficiency, and robustness. However, FL adoption still faces critical challenges including communication overhead, non-IID data, sensor unreliability, and adversarial threats. To address these, we highlight key research directions such as energy-aware FL protocols, scalable and secure aggregation, real-world testbeds, and incentive mechanisms. By bridging insights from AI, wireless networking, and environmental science, this review lays the groundwork for establishing FL as a cornerstone of sustainable, privacy-aware, and intelligent environmental monitoring systems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/ojcs.2026.3690680first seen 2026-06-29 07:56:13
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