{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T14:05:22Z","timestamp":1787321122755,"version":"build-2736575974"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819224968","type":"print"},{"value":"9789819224975","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-2497-5_10","type":"book-chapter","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T12:04:58Z","timestamp":1783512298000},"page":"171-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TimeGFD: A Lightweight Client-Side Time-Series Forecasting Framework Based on\u00a0Generative Federated Distillation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6785-5665","authenticated-orcid":false,"given":"Junling","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4626-4373","authenticated-orcid":false,"given":"Xiaocao","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2910-3447","authenticated-orcid":false,"given":"Jia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7090-0325","authenticated-orcid":false,"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9031-107X","authenticated-orcid":false,"given":"Wei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Ali, M., Lisle, C., Moore, P.W., Barkouki, T., Kirkwood, B.J., Brattain, L.J.: Finetuning foundation models with federated learning for privacy preserving medical time series forecasting. In: 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1\u20137. IEEE (2025)","DOI":"10.1109\/EMBC58623.2025.11254049"},{"key":"10_CR2","unstructured":"Ansari, A.F., et al.: Chronos-2: from univariate to universal forecasting. arXiv preprint arXiv:2510.15821 (2025)"},{"key":"10_CR3","unstructured":"Ansari, A.F., et al.: Chronos: learning the language of time series. arXiv preprint arXiv:2403.07815 (2024)"},{"issue":"2","key":"10_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3589316","volume":"1","author":"D Campos","year":"2023","unstructured":"Campos, D., Zhang, M., Yang, B., Kieu, T., Guo, C., Jensen, C.S.: Lightts: lightweight time series classification with adaptive ensemble distillation. Proc. ACM Manage. Data 1(2), 1\u201327 (2023)","journal-title":"Proc. ACM Manage. Data"},{"key":"10_CR5","unstructured":"Chen, S., Long, G., Jiang, J.: Fedal: federated dataset learning for time series foundation models. arXiv preprint arXiv:2508.04045 (2025)"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Chen, S., Long, G., Jiang, J., Zhang, C.: Federated foundation models on heterogeneous time series. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 39, pp. 15839\u201315847 (2025)","DOI":"10.1609\/aaai.v39i15.33739"},{"key":"10_CR7","unstructured":"Desai, A., Freeman, C., Wang, Z., Beaver, I.: Timevae: a variational auto-encoder for multivariate time series generation. arXiv preprint arXiv:2111.08095 (2021)"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Ekambaram, V., Jati, A., Nguyen, N., Sinthong, P., Kalagnanam, J.: Tsmixer: lightweight MLP-mixer model for multivariate time series forecasting. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, pp. 459\u2013469 (2023)","DOI":"10.1145\/3580305.3599533"},{"key":"10_CR9","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429\u2013450 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10_CR10","unstructured":"Liang, D., et al.: The forecast after the forecast: a post-processing shift in time series. arXiv preprint arXiv:2601.20280 (2026)"},{"key":"10_CR11","doi-asserted-by":"publisher","first-page":"94512","DOI":"10.52202\/079017-2996","volume":"37","author":"Q Liu","year":"2024","unstructured":"Liu, Q., Liu, X., Liu, C., Wen, Q., Liang, Y.: Time-FFM: Towards LM-empowered federated foundation model for time series forecasting. Adv. Neural. Inf. Process. Syst. 37, 94512\u201394538 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"8","key":"10_CR12","doi-asserted-by":"publisher","first-page":"6348","DOI":"10.1109\/JIOT.2020.3011726","volume":"8","author":"Y Liu","year":"2020","unstructured":"Liu, Y., et al.: Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach. IEEE Internet Things J. 8(8), 6348\u20136358 (2020)","journal-title":"IEEE Internet Things J."},{"key":"10_CR13","unstructured":"Liu, Y., Qin, G., Huang, X., Wang, J., Long, M.: Timer-xl: long-context transformers for unified time series forecasting. arXiv preprint arXiv:2410.04803 (2024)"},{"key":"10_CR14","unstructured":"Liu, Y., et al.: Sundial: a family of highly capable time series foundation models. arXiv preprint arXiv:2502.00816 (2025)"},{"key":"10_CR15","unstructured":"Liu, Y., Zhang, H., Li, C., Huang, X., Wang, J., Long, M.: Timer: generative pretrained transformers are large time series models. arXiv preprint arXiv:2402.02368 (2024)"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Ma, X., Chen, T., Wang, P., Li, X., Zhang, C.: Recast: reliability-aware codebook assisted lightweight time series forecasting. arXiv preprint arXiv:2511.11991 (2025)","DOI":"10.1609\/aaai.v40i29.39610"},{"key":"10_CR17","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Wang, B., et al.: Non-collective calibrating strategy for time series forecasting. In: Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, pp. 3335\u20133343 (2025)","DOI":"10.24963\/ijcai.2025\/371"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Wen, Q., Yang, L., Zhou, T., Sun, L.: Robust time series analysis and applications: an industrial perspective. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4836\u20134837 (2022)","DOI":"10.1145\/3534678.3542612"},{"key":"10_CR20","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., Long, M.: Timesnet: temporal 2D variation modeling for general time series analysis. arXiv preprint arXiv:2210.02186 (2022)"},{"key":"10_CR21","unstructured":"Xu, Z., Zeng, A., Xu, Q.: Fits: Modeling time series with $$10 k $$ parameters. arXiv preprint arXiv:2307.03756 (2023)"},{"key":"10_CR22","unstructured":"Yuan, W., Ye, G., Zhao, X., Nguyen, Q.V.H., Cao, Y., Yin, H.: Tackling data heterogeneity in federated time series forecasting. arXiv preprint arXiv:2411.15716 (2024)"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a037, pp. 11121\u201311128 (2023)","DOI":"10.1609\/aaai.v37i9.26317"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Engineering for Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-2497-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T13:06:11Z","timestamp":1787317571000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-2497-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"ISBN":["9789819224968","9789819224975"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-2497-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]},"assertion":[{"value":"9 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"FLINS-ISKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems and Knowledge Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iske2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2026.flins.cc","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}