{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:11:45Z","timestamp":1772835105732,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819543830","type":"print"},{"value":"9789819543847","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-4384-7_18","type":"book-chapter","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T15:43:01Z","timestamp":1762357381000},"page":"250-264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fitness-Driven Evolutionary Federated Learning: Adaptive Client Selection and\u00a0Dynamic Population for\u00a0Communication Efficiency"],"prefix":"10.1007","author":[{"given":"Yichun","family":"Yu","sequence":"first","affiliation":[]},{"given":"Yuqing","family":"Lan","sequence":"additional","affiliation":[]},{"given":"Xiaoyi","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhihuan","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Han","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"key":"18_CR1","unstructured":"Achille, A., Rovere, M., Soatto, S.: Critical learning periods in deep networks. In: International Conference on Learning Representations (2018)"},{"key":"18_CR2","unstructured":"Alistarh, D., Grubic, D., Li, J., Tomioka, R., Vojnovic, M.: QSGD: communication-efficient SGD via gradient quantization and encoding. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"18_CR3","unstructured":"Bradbury, J., Frostig, R.: JAX: composable transformations of Python+NumPy programs. http:\/\/github.com\/google\/jax (2018)"},{"key":"18_CR4","unstructured":"Chai, Z.Y., Yang, C.D., Li, Y.I.: Communication efficiency optimization in federated learning based on multi-objective evolutionary algorithm. Evol. Intell., 1\u201312 (2022)"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"De\u00a0Falco, I., et al.: A federated learning-inspired evolutionary algorithm: application to glucose prediction. Sensors, 2957 (2023)","DOI":"10.3390\/s23062957"},{"issue":"6","key":"18_CR6","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1109\/TIT.1976.1055638","volume":"22","author":"W Diffie","year":"1976","unstructured":"Diffie, W., Hellman, M.E.: New directions in cryptography. IEEE Trans. Inf. Theory 22(6), 644\u2013654 (1976)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Imteaj, A., Ahmed: Federated learning for resource-constrained IoT devices: panoramas and state of the art. In: Federated and Transfer Learning, pp. 7\u201327. Springer (2022)","DOI":"10.1007\/978-3-031-11748-0_2"},{"key":"18_CR8","unstructured":"Kone\u010dn\u1ef3, J.: Federated learning: strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)"},{"key":"18_CR9","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. University of Toronto, Tech. rep. (2009)"},{"key":"18_CR10","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS) (2012)"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Lange, R.T.: evosax: JAX - based evolution strategies. arXiv preprint arXiv:2212.04180 (2022)","DOI":"10.1145\/3583133.3590733"},{"key":"18_CR12","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Fair resource allocation in federated learning. arXiv preprint arXiv:1905.10497 (2019)"},{"key":"18_CR13","unstructured":"Malinovskiy, G., Kovalev, D., Gasanov, E., Condat, L., Richtarik, P.: From local sgd to local fixed-point methods for federated learning. In: ICML 2020 (2020)"},{"issue":"1","key":"18_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-018-04316-3","volume":"9","author":"DC Mocanu","year":"2018","unstructured":"Mocanu, D.C.: Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nat. Commun. 9(1), 1\u201312 (2018)","journal-title":"Nat. Commun."},{"key":"18_CR15","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1007\/s10208-015-9296-2","volume":"17","author":"Y Nesterov","year":"2017","unstructured":"Nesterov, Y., Spokoiny, V.: Random gradient-free minimization of convex functions. Found. Comput. Math. 17, 527\u2013566 (2017)","journal-title":"Found. Comput. Math."},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Rahimi, M.M., Bhatti, H.I., Park, Y., Kousar, H., Moon, J.: EvoFed: leveraging evolutionary strategies for communication-efficient federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (2024)","DOI":"10.52202\/075280-2726"},{"key":"18_CR17","unstructured":"Risi, S., Togelius, J.: Neuro evolution in games: state of the art and open challenges. CoRR (2014)"},{"key":"18_CR18","unstructured":"Salimans, T., Ho, J., Chen, X., Sidor, S., Sutskever, I.: Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864 (2017)"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Sattler, F., Wiedemann, S., M\u00fcller, K.R., Samek, W.: Sparse binary compression: Towards distributed deep learning with minimal communication. In: IJCNN 2019, pp.\u00a01\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852172"},{"issue":"4","key":"18_CR20","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.neunet.2009.12.004","volume":"23","author":"F Sehnke","year":"2010","unstructured":"Sehnke, F., Osendorfer, C.: Parameter-exploring policy gradients. Neural Netw. 23(4), 551\u2013559 (2010). https:\/\/doi.org\/10.1016\/j.neunet.2009.12.004","journal-title":"Neural Netw."},{"key":"18_CR21","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"18_CR22","unstructured":"Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.S.: Federated multi-task learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"18_CR23","unstructured":"Stich, S.U.: Local SGD converges fast and communicates little. arXiv preprint arXiv:1805.09767 (2018)"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Wang, H., Kaplan, Z., Niu, D., Li, B.: Optimizing federated learning on Non-IID data with reinforcement learning. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 1698\u20131707. IEEE (2020)","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"18_CR25","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated learning with matched averaging. arXiv preprint arXiv:2002.06440 (2020)"},{"issue":"1","key":"18_CR26","first-page":"949","volume":"15","author":"D Wierstra","year":"2014","unstructured":"Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., Schmidhuber, J.: Natural evolution strategies. J. Mach. Learn. Res. 15(1), 949\u2013980 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"18_CR27","doi-asserted-by":"crossref","unstructured":"Wierstra, D., Schaul, T., Peters, J., Schmidhuber, J.: Fitness expectation maximization. In: Parallel Problem Solving from Nature\u2013PPSN X: 10th International Conference, pp. 337\u2013346. Springer (2008)","DOI":"10.1007\/978-3-540-87700-4_34"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. In: Reinforcement Learning, pp. 5\u201332. Springer (1992)","DOI":"10.1007\/978-1-4615-3618-5_2"},{"key":"18_CR29","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Yan, G., Wang, H., Li, J.: Seizing critical learning periods in federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, issue 8, pp. 8788\u20138796 (2022)","DOI":"10.1609\/aaai.v36i8.20859"},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"Yan, G., Wang, H., Yuan, X., Li, J.: CriticalFL: a critical learning periods augmented client selection framework for efficient federated learning. In: Proceedings of the ACM KDD (2023)","DOI":"10.1145\/3580305.3599293"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Zhu, H., Jin, Y.: Multi-objective evolutionary federated learning. IEEE Trans. Neural Networks Learn. Syst., 1310\u20131322 (2019)","DOI":"10.1109\/TNNLS.2019.2919699"},{"key":"18_CR33","doi-asserted-by":"crossref","unstructured":"Zhu, H., Jin, Y.: Real-time federated evolutionary neural architecture search. IEEE Trans. Evol. Comput., 364\u2013378 (2021)","DOI":"10.1109\/TEVC.2021.3099448"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4384-7_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T14:09:06Z","timestamp":1772806146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4384-7_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"ISBN":["9789819543830","9789819543847"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4384-7_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"6 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Okinawa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"20 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2025.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}