{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T07:16:19Z","timestamp":1763968579945,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031695827"},{"type":"electronic","value":"9783031695834"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-69583-4_14","type":"book-chapter","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T19:02:05Z","timestamp":1724612525000},"page":"196-211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Joint Approach to\u00a0Local Updating and\u00a0Gradient Compression for\u00a0Efficient Asynchronous Federated Learning"],"prefix":"10.1007","author":[{"given":"Jiajun","family":"Song","sequence":"first","affiliation":[]},{"given":"Jiajun","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Rongwei","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Shuzhao","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Abdelmoniem, A.M., Canini, M.: DC2: delay-aware compression control for distributed machine learning. In: IEEE INFOCOM 2021-IEEE Conference on Computer Communications, pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/INFOCOM42981.2021.9488810"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Aji, A.F., Heafield, K.: Sparse communication for distributed gradient descent (2017). arXiv preprint arXiv:1704.05021","DOI":"10.18653\/v1\/D17-1045"},{"key":"14_CR3","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":"14_CR4","unstructured":"Bernstein, J., Wang, Y.X., Azizzadenesheli, K., Anandkumar, A.: signSGD: compressed optimisation for non-convex problems. In: International Conference on Machine Learning, pp. 560\u2013569. PMLR (2018)"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Chai, Z., Chen, Y., Anwar, A., Zhao, L., Cheng, Y., Rangwala, H.: FedAT: a high-performance and communication-efficient federated learning system with asynchronous tiers. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1\u201316 (2021)","DOI":"10.1145\/3458817.3476211"},{"key":"14_CR6","unstructured":"Chen, M., Mao, B., Ma, T.: Efficient and robust asynchronous federated learning with stragglers. In: International Conference on Learning Representations (2019)"},{"key":"14_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.future.2021.02.012","volume":"120","author":"M Chen","year":"2021","unstructured":"Chen, M., Mao, B., Ma, T.: FedSA: a staleness-aware asynchronous federated learning algorithm with non-IID data. Futur. Gener. Comput. Syst. 120, 1\u201312 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ning, Y., Slawski, M., Rangwala, H.: Asynchronous online federated learning for edge devices with non-IID data. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 15\u201324. IEEE (2020)","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Cui, L., Su, X., Zhou, Y., Liu, J.: Optimal rate adaption in federated learning with compressed communications. In: IEEE INFOCOM 2022-IEEE Conference on Computer Communications, pp. 1459\u20131468. IEEE (2022)","DOI":"10.1109\/INFOCOM48880.2022.9796982"},{"key":"14_CR10","unstructured":"van Dijk, M., Nguyen, N.V., Nguyen, T.N., Nguyen, L.M., Tran-Dinh, Q., Nguyen, P.H.: Asynchronous federated learning with reduced number of rounds and with differential privacy from less aggregated gaussian noise (2020). arXiv preprint arXiv:2007.09208"},{"key":"14_CR11","unstructured":"Hsieh, K., Harlap, A., Vijaykumar, N., Konomis, D., Ganger, G.R., Gibbons, P.B., Mutlu, O.: Gaia:$$\\{$$Geo-Distributed$$\\}$$ machine learning approaching $$\\{$$LAN$$\\}$$ speeds. In: 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17), pp. 629\u2013647 (2017)"},{"key":"14_CR12","unstructured":"Hsu, T.M.H., Qi, H., Brown, M.: Measuring the effects of non-identical data distribution for federated visual classification (2019). arXiv preprint arXiv:1909.06335"},{"issue":"4","key":"14_CR13","doi-asserted-by":"publisher","first-page":"874","DOI":"10.1109\/JSAC.2023.3242719","volume":"41","author":"CH Hu","year":"2023","unstructured":"Hu, C.H., Chen, Z., Larsson, E.G.: Scheduling and aggregation design for asynchronous federated learning over wireless networks. IEEE J. Sel. Areas Commun. 41(4), 874\u2013886 (2023)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"14_CR14","first-page":"17202","volume":"35","author":"A Koloskova","year":"2022","unstructured":"Koloskova, A., Stich, S.U., Jaggi, M.: Sharper convergence guarantees for asynchronous SGD for distributed and federated learning. Adv. Neural. Inf. Process. Syst. 35, 17202\u201317215 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR15","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Ramage, D., Richt\u00e1rik, P.: Federated optimization: Distributed machine learning for on-device intelligence (2016). arXiv preprint arXiv:1610.02527"},{"key":"14_CR16","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Yu, F.X., Richt\u00e1rik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency (2016). arXiv preprint arXiv:1610.05492"},{"issue":"3","key":"14_CR17","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: challenges, methods, and future directions. IEEE Signal Process. Mag. 37(3), 50\u201360 (2020)","journal-title":"IEEE Signal Process. Mag."},{"key":"14_CR18","unstructured":"Lian, X., Huang, Y., Li, Y., Liu, J.: Asynchronous parallel stochastic gradient for nonconvex optimization. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"14_CR19","unstructured":"Lin, Y., Han, S., Mao, H., Wang, Y., Dally, W.J.: Deep gradient compression: Reducing the communication bandwidth for distributed training (2017). arXiv preprint arXiv:1712.01887"},{"key":"14_CR20","unstructured":"Lu, R., Song, J., Chen, B., Cui, L., Wang, Z.: DAGC: Data-aware adaptive gradient compression"},{"key":"14_CR21","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":"14_CR22","unstructured":"Nguyen, J., et al.: Federated learning with buffered asynchronous aggregation. In: International Conference on Artificial Intelligence and Statistics, pp. 3581\u20133607. PMLR (2022)"},{"issue":"8","key":"14_CR23","doi-asserted-by":"publisher","first-page":"5168","DOI":"10.1109\/TCOMM.2021.3083316","volume":"69","author":"MK Nori","year":"2021","unstructured":"Nori, M.K., Yun, S., Kim, I.M.: Fast federated learning by balancing communication trade-offs. IEEE Trans. Commun. 69(8), 5168\u20135182 (2021)","journal-title":"IEEE Trans. Commun."},{"issue":"9","key":"14_CR24","doi-asserted-by":"publisher","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","volume":"31","author":"F Sattler","year":"2019","unstructured":"Sattler, F., Wiedemann, S., M\u00fcller, K.R., Samek, W.: Robust and communication-efficient federated learning from non-i.i.d. data. IEEE Trans. Neural Netw. Learn. Syst. 31(9), 3400\u20133413 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"14_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556"},{"issue":"9","key":"14_CR26","doi-asserted-by":"publisher","first-page":"6961","DOI":"10.1109\/TWC.2022.3153495","volume":"21","author":"Z Wang","year":"2022","unstructured":"Wang, Z., et al.: Asynchronous federated learning over wireless communication networks. IEEE Trans. Wireless Commun. 21(9), 6961\u20136978 (2022)","journal-title":"IEEE Trans. Wireless Commun."},{"key":"14_CR27","unstructured":"Warden, P.: Speech commands: A dataset for limited-vocabulary speech recognition (2018). arXiv preprint arXiv:1804.03209"},{"key":"14_CR28","unstructured":"Wen, W., et al.: TernGrad: ternary gradients to reduce communication in distributed deep learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Wu, D., Ullah, R., Rodgers, P., Kilpatrick, P., Spence, I., Varghese, B.: Communication efficient DNN partitioning-based federated learning (2023). arXiv preprint arXiv:2304.05495","DOI":"10.1109\/TPDS.2024.3349617"},{"issue":"5","key":"14_CR30","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1109\/TC.2020.2994391","volume":"70","author":"W Wu","year":"2020","unstructured":"Wu, W., He, L., Lin, W., Mao, R., Maple, C., Jarvis, S.: SAFA: a semi-asynchronous protocol for fast federated learning with low overhead. IEEE Trans. Comput. 70(5), 655\u2013668 (2020)","journal-title":"IEEE Trans. Comput."},{"key":"14_CR31","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms (2017). arXiv preprint arXiv:1708.07747"},{"key":"14_CR32","unstructured":"Xie, C., Koyejo, S., Gupta, I.: Asynchronous federated optimization (2019). arXiv preprint arXiv:1903.03934"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Xu, Y., Liao, Y., Xu, H., Ma, Z., Wang, L., Liu, J.: Adaptive control of local updating and model compression for efficient federated learning. IEEE Trans. Mob. Comput. 22(10), 5675\u20135689 (2022)","DOI":"10.1109\/TMC.2022.3186936"},{"key":"14_CR34","unstructured":"Zhang, W., Gupta, S., Lian, X., Liu, J.: Staleness-aware Async-SGD for distributed deep learning (2015). arXiv preprint arXiv:1511.05950"},{"issue":"3","key":"14_CR35","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1109\/TPDS.2023.3237752","volume":"34","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., et al.: FedMDS: an efficient model discrepancy-aware semi-asynchronous clustered federated learning framework. IEEE Trans. Parallel Distrib. Syst. 34(3), 1007\u20131019 (2023)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"14_CR36","unstructured":"Zheng, S., et al.: Asynchronous stochastic gradient descent with delay compensation. In: International Conference on Machine Learning, pp. 4120\u20134129. PMLR (2017)"},{"issue":"3","key":"14_CR37","doi-asserted-by":"publisher","first-page":"314","DOI":"10.3390\/electronics11030314","volume":"11","author":"F Zhu","year":"2022","unstructured":"Zhu, F., Hao, J., Chen, Z., Zhao, Y., Chen, B., Tan, X.: STAFL: staleness-tolerant asynchronous federated learning on non-iid dataset. Electronics 11(3), 314 (2022)","journal-title":"Electronics"}],"container-title":["Lecture Notes in Computer Science","Euro-Par 2024: Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-69583-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T19:03:55Z","timestamp":1724612635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-69583-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031695827","9783031695834"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-69583-4_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"26 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Euro-Par","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"europar2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.euro-par.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}