{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T03:00:40Z","timestamp":1777950040589,"version":"3.51.4"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T00:00:00Z","timestamp":1777766400000},"content-version":"vor","delay-in-days":122,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>\n                    Accurate Environmental, Social, and Governance (ESG) forecasting is pivotal for modern sustainable finance, yet it remains hampered by data heterogeneity, outlier distortions, and strict privacy regulations that inhibit cross\u2010institutional data sharing. This study addresses these limitations by proposing a comprehensive framework that synergizes advanced ensemble learning with privacy\u2010preserving federated architectures. First, a centralized \u201cHSE\u2010ESG\u201d is developed, utilizing a stacking ensemble of LightGBM, XGBoost, and multilayer perceptrons, synthesized by a Bayesian Ridge metalearner with a unique feature passthrough mechanism to capture complex nonlinear dependencies efficiently. Subsequently, to mitigate data leakage risks and address data silos, the framework transitions to \u201cFed\u2010ESGNet,\u201d employing a novel Federated Dynamic Weighted Averaging (FedDWA) algorithm that aggregates client updates based on local validation performance rather than traditional sample volume weighting. Empirical analysis using a longitudinal Refinitiv dataset (2010\u20132022) reveals that the centralized model achieves a remarkable coefficient of determination (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    ) of 0.9854, while the federated approach maintains near\u2010parity performance\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.9799 despite data fragmentation, significantly outperforming standalone baselines. Furthermore, the integration of Explainable AI (XAI) techniques, specifically SHAP and LIME, guarantees granular decision\u2010making transparency, establishing a robust, scalable, and interpretable paradigm for secure ESG governance assessment.\n                  <\/jats:p>","DOI":"10.1155\/acis\/8836162","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:41:41Z","timestamp":1777873301000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging Data Silos in Corporate Governance: A Hierarchical Stacking Ensemble With Federated Dynamic Aggregation"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8642-5688","authenticated-orcid":false,"given":"Abdul Kadar Muhammad","family":"Masum","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3463-2738","authenticated-orcid":false,"given":"Md. Abul Kalam","family":"Azad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8138-5836","authenticated-orcid":false,"given":"Md. Tofael Ahmed","family":"Bhuiyan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8595-5772","authenticated-orcid":false,"given":"Md. Abdur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,5,3]]},"reference":[{"key":"e_1_2_16_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12599-024-00852-z"},{"key":"e_1_2_16_2_2","doi-asserted-by":"publisher","DOI":"10.1017\/err.2022.13"},{"key":"e_1_2_16_3_2","volume-title":"ESG and Supply Chains in 2024: Key Trends, Challenges, and Future Outlook","author":"Barnes","year":"2025"},{"key":"e_1_2_16_4_2","unstructured":"Directive 2024\/1760 Directive (EU) 2024\/1760 of the European Parliament and of the Council on Corporate Sustainability due Diligence 2024 Official Journal of the European Union."},{"key":"e_1_2_16_5_2","article-title":"Time to Get to Know Your Supply Chain","author":"White & Case","year":"2024","journal-title":"EU Adopts Corporate Sustainability Due Diligence Directive"},{"key":"e_1_2_16_6_2","volume-title":"Corporate Sustainability Due 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Machine Learning and Systems"},{"key":"e_1_2_16_22_2","first-page":"7611","article-title":"Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization","volume":"33","author":"Wang J.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_16_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/mis.2021.3082561"}],"container-title":["Applied Computational Intelligence and Soft 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