{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:04:27Z","timestamp":1750219467886,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819681853","type":"print"},{"value":"9789819681860","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-8186-0_4","type":"book-chapter","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T10:15:24Z","timestamp":1750155324000},"page":"40-51","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedDPG: An Adaptive Yet Efficient Prompt-Tuning Approach in\u00a0Federated Learning Settings"],"prefix":"10.1007","author":[{"given":"Ali","family":"Shakeri","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0406-5974","authenticated-orcid":false,"given":"Wei Emma","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5988-5494","authenticated-orcid":false,"given":"Amin","family":"Beheshti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1001-7925","authenticated-orcid":false,"given":"Weitong","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4408-1952","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Lishan","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"4_CR1","volume-title":"Decentralized federated learning: fundamentals, state of the art, frameworks, trends, and challenges","author":"E Beltr\u00e1n","year":"2023","unstructured":"Beltr\u00e1n, E., P\u00e9rez, M.Q., S\u00e1nchez, P., Bernal, S.L., Bovet, G., P\u00e9rez, M.G.: Decentralized federated learning: fundamentals, state of the art, frameworks, trends, and challenges. IEEE Commun. Surv, Tutorials (2023)"},{"key":"4_CR2","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P.: Language models are few-shot learners. In: Proc. of Neural Information Processing Systems, NeurIPS (2020)"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Che, T., Liu, J., Zhou, Y., Ren, J., Zhou, J., Sheng, V.S.: Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization. In: Proc. of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.488"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y.: Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models. CoRR (2022)","DOI":"10.21203\/rs.3.rs-1553541\/v1"},{"key":"4_CR5","unstructured":"Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A.: The Llama 3 Herd of Models (2024). https:\/\/arxiv.org\/abs\/2407.21783"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Fu, Z., Yang, H., So, A.M., Lam, W., Bing, L., Collier, N.: On the effectiveness of parameter-efficient fine-tuning. In: Proc. of the Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI (2023)","DOI":"10.1609\/aaai.v37i11.26505"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Gao, T., Fisch, A., Chen, D.: Making pre-trained language models better few-shot learners. In: Proc. of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL\/IJCNLP (2021)","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"4_CR8","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S.: LoRA: low-rank adaptation of large language models. In: Proc. of the Tenth International Conference on Learning Representations, ICLR (2022)"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. In: Proc. of the Conference on Empirical Methods in Natural Language Processing, EMNLP (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proc. of the 58th Annual Meeting of the Association for Computational Linguistics, ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Li, X.L., Liang, P.: Prefix-tuning: optimizing continuous prompts for generation. In: Proc. of the 59th Annual Meeting of the Association for Computational Linguistics ACL (2021)","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"4_CR12","volume-title":"Federated Learning in Mobile Edge Networks: A Comprehensive Survey","author":"W Lim","year":"2020","unstructured":"Lim, W., Luong, N.C., Hoang, D.T., Jiao, Y., Liang, Y., Yang, Q.: Federated Learning in Mobile Edge Networks: A Comprehensive Survey. IEEE Commun. Surv, Tutorials (2020)"},{"key":"4_CR13","unstructured":"Liu, H., Tam, D., Muqeeth, M., Mohta, J., Huang, T., Bansal, M.: Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. In: Proc. of the Neural Information Processing Systems, NeurIPS (2022)"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z.: P-tuning: prompt tuning can be comparable to fine-tuning across scales and tasks. In: Proc. of the 60th Annual Meeting of the Association for Computational Linguistics, ACL (2022)","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"4_CR15","volume-title":"GPT Understands","author":"X Liu","year":"2021","unstructured":"Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z.: GPT Understands. Too, CoRR (2021)"},{"key":"4_CR16","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D.: RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR (2019)"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Ma, X., Zhu, J., Lin, Z., Chen, S., Qin, Y.: A state-of-the-art survey on solving non-IID data in Federated Learning. Future Gener. Comput. Syst. (2022)","DOI":"10.1016\/j.future.2022.05.003"},{"key":"4_CR18","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Proc. of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS (2017)"},{"key":"4_CR19","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G.: PyTorch: an imperative style, high-performance deep learning library. In: Proc. of Neural Information Processing Systems, NeurIPS (2019)"},{"key":"4_CR20","unstructured":"Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. (2020)"},{"key":"4_CR21","unstructured":"Rebuffi, S., Bilen, H., Vedaldi, A.: Learning multiple visual domains with residual adapters. In: Proc. of the Neural Information Processing Systems, NeurIPS (2017)"},{"key":"4_CR22","doi-asserted-by":"crossref","unstructured":"Shakeri, A., Chen, P., Shu, Y., Yang, L., Zhang, W.E., Chen, W.: Transforming data product generation through federated learning: an exploration of FL applications in data ecosystems. In: Proc. of the International Conference on Web Services, ICWS (2024)","DOI":"10.1109\/ICWS62655.2024.00027"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A.Y.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proc. of the Conference on Empirical Methods in Natural Language Processing, EMNLP, A meeting of SIGDAT, a Special Interest Group of the ACL (2013)","DOI":"10.18653\/v1\/D13-1170"},{"key":"4_CR24","unstructured":"Sun, J., Xu, Z., Yin, H., Yang, D., Xu, D., Liu, Y.: FedBPT: efficient federated black-box prompt tuning for large language models. In: Proc. of the Forty-first International Conference on Machine Learning, ICML (2024)"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Tan, A.Z., Yu, H., Cui, L., Yang, Q.: Towards personalized federated learning. IEEE Trans. Neural Networks Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Woisetschl\u00e4ger, H., Erben, A., Wang, S., Mayer, R., Jacobsen, H.: A survey on efficient federated learning methods for foundation model training. In: Proc. of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI (2024)","DOI":"10.24963\/ijcai.2024\/919"},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A.: HuggingFace\u2019s Transformers: State-of-the-art Natural Language Processing. CoRR (2019)","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst, Technol. (2019)","DOI":"10.1145\/3298981"},{"key":"4_CR29","doi-asserted-by":"crossref","unstructured":"Yin, X., Zhu, Y., Hu, J.: A comprehensive survey of privacy-preserving federated learning: a taxonomy, review, and future directions. ACM Comput. Surv. (2022)","DOI":"10.1145\/3460427"},{"key":"4_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, J.O., Sax, A., Zamir, A., Guibas, L.J., Malik, J.: Side-tuning: a baseline for network adaptation via additive side networks. In: Proc. of the 16th European Conference on Computer Vision, ECCV (2020)","DOI":"10.1007\/978-3-030-58580-8_41"},{"key":"4_CR31","unstructured":"Zhang, X., Zhao, J.J., LeCun, Y.: Character-level convolutional networks for text classification. In: Proc. of the Neural Information Processing Systems, NIPS (2015)"},{"key":"4_CR32","doi-asserted-by":"crossref","unstructured":"Zhao, H., Du, W., Li, F., Li, P., Liu, G.: FedPrompt: communication-efficient and privacy-preserving prompt tuning in federated learning. In: Proc. of the International Conference on Acoustics, Speech and Signal Processing, ICASSP (2023)","DOI":"10.1109\/ICASSP49357.2023.10095356"},{"key":"4_CR33","unstructured":"Zhao, W., Chen, Y., Lee, R., Qiu, X., Gao, Y., Fan, H.: Breaking physical and linguistic borders: multilingual federated prompt tuning for low-resource languages. In: Proc. of the Twelfth International Conference on Learning Representations, ICLR (2024)"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-8186-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T10:15:42Z","timestamp":1750155342000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-8186-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819681853","9789819681860"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-8186-0_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"18 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}