{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:32:24Z","timestamp":1774629144800,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R and D Program of China","award":["2020YFB1807805"],"award-info":[{"award-number":["2020YFB1807805"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171049"],"award-info":[{"award-number":["62171049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous anti poisoning federated deep learning framework to pursue both efficiency and security. This paper proposes a lightweight dynamic asynchronous algorithm considering the averaging frequency control and parameter selection for federated learning to speed up model averaging and improve efficiency, which enables federated learning to adaptively remove the stragglers with low computing power, bad channel conditions, or anomalous parameters. In addition, a novel local reliability mutual evaluation mechanism is presented to enhance the security of poisoning attacks, which enables federated learning to detect the anomalous parameter of poisoning attacks and adjust the weight proportion of in model aggregation based on evaluation score. The experiment results on three datasets illustrate that our design can reduce the training time by 30% and is robust to the representative poisoning attacks significantly, confirming the applicability of our scheme.<\/jats:p>","DOI":"10.3390\/s22020684","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T20:49:21Z","timestamp":1642452561000},"page":"684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Dynamic Asynchronous Anti Poisoning Federated Deep Learning with Blockchain-Based Reputation-Aware Solutions"],"prefix":"10.3390","volume":"22","author":[{"given":"Zunming","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ensen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"ref_1","unstructured":"Lueth, K. (2021, December 01). State of the Iot 2020: Number of Iot Devices Now at 7b-Market Accelerating. Available online: https:\/\/iot-analytics.com\/state-of-the-iot-2020-12-billion-iot-connections-surpassing-non-iot-for-the-first-time\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TWC.2020.3024629","article-title":"A Joint Learning and Communications Framework for Federated Learning over Wireless Networks","volume":"20","author":"Chen","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","article-title":"Adaptive federated learning in resource constrained edge computing systems","volume":"37","author":"Wang","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nishio, T., and Yonetani, R. (2019, January 20\u201324). Client selection for federated learning with heterogeneous resources in mobile edge. Proceedings of the ICC 2019-2019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, Y., Hong, Y., and Park, J. (2021). Efficient gradient updating strategies with adaptive power allocation for federated learning over wireless backhaul. Sensors, 21.","DOI":"10.3390\/s21206791"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.1109\/TII.2019.2942179","article-title":"Differentially private asynchronous federated learning for mobile edge computing in urban informatics","volume":"16","author":"Lu","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_7","unstructured":"Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V. (2020, January 3\u20135). How to backdoor federated learning. Proceedings of the International Conference on Artificial Intelligence and Statistics, Palermo, Italy."},{"key":"ref_8","unstructured":"Shen, S., Tople, S., and Saxena, P. (2016, January 5\u20138). Auror: Defending against poisoning attacks in collaborative deep learning systems. Proceedings of the 32nd Annual Conference on Computer Security Applications, Los Angeles, CA, USA."},{"key":"ref_9","unstructured":"Fung, C., Yoon, C.J., and Beschastnikh, I. (2020). Mitigating sybils in federated learning poisoning. arXiv."},{"key":"ref_10","unstructured":"Blanchard, P., el Mhamdi, E.M., Guerraoui, R., and Stainer, J. (2017, January 4\u20139). Machine learning with adversaries: Byzantine tolerant gradient descent. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106854","DOI":"10.1016\/j.cie.2020.106854","article-title":"A review of applications in federated learning","volume":"149","author":"Li","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/MNET.2019.1800286","article-title":"In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning","volume":"33","author":"Wang","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_13","unstructured":"Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., and Kim, S.-L. (2019). Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. arXiv."},{"key":"ref_14","unstructured":"Caldas, S., Kone\u010dny, J., McMahan, H.B., and Talwalkar, A. (2019). Expanding the reach of federated learning by reducing client resource requirements. arXiv."},{"key":"ref_15","first-page":"1","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"2020","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_16","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. (2021). Federated optimization in heterogeneous networks. arXiv."},{"key":"ref_17","unstructured":"Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., and Khazaeni, Y. (2020). Federated learning with matched averaging. arXiv."},{"key":"ref_18","unstructured":"Lian, X., Zhang, W., Zhang, C., and Liu, J. (2018, January 10\u201315). Asynchronous decentralized parallel stochastic gradient descent. Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_19","unstructured":"Zheng, S., Meng, Q., Wang, T., Chen, W., Yu, N., Ma, Z.-M., and Liu, T.-Y. (2017, January 6\u201311). Asynchronous stochastic gradient descent with delay compensation. Proceedings of the 34th International Conference on Machine Learning, Sydney, NSW, Australia."},{"key":"ref_20","unstructured":"Xie, C., Koyejo, S., and Gupta, I. (2020). Asynchronous federated optimization. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/MCOM.001.1900091","article-title":"Data security issues in deep learning: Attacks, countermeasures, and opportunities","volume":"57","author":"Xu","year":"2021","journal-title":"IEEE Commun. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mohassel, P., and Zhang, Y. (2017, January 22\u201326). Secureml: A system for scalable privacy-preserving machine learning. Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.","DOI":"10.1109\/SP.2017.12"},{"key":"ref_23","first-page":"1333","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"Aono","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_24","first-page":"33","article-title":"Distributed learning without distress: Privacy-preserving empirical risk minimization","volume":"7","author":"Jayaraman","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1109\/TIFS.2019.2939713","article-title":"Privacy-preserving collaborative deep learning with unreliable participants","volume":"15","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bonawitz, K., Ivanov, V., Kreuter, B., and Marcedone, A. (November, January 30). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA.","DOI":"10.1145\/3133956.3133982"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yu, D., Zhang, H., Chen, W., Liu, T.-Y., and Yin, J. (2020). Gradient perturbation is underrated for differentially private convex optimization. arXiv.","DOI":"10.24963\/ijcai.2020\/431"},{"key":"ref_28","unstructured":"Paillier, P. (1999, January 2\u20136). Public-key cryptosystems based on composite degree residuosity classes. Proceedings of the International Conference on the Theory and Applications of Cryptographic Techniques, Prague, Czech Republic."},{"key":"ref_29","unstructured":"Acs, G., and Castelluccia, C. (2018). I have a dream (differentially private smart metering). International Workshop on Information Hiding, Springer."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TDSC.2015.2484326","article-title":"A comprehensive comparison of multiparty secure additions with differential privacy","volume":"14","author":"Goryczka","year":"2019","journal-title":"IEEE Trans. Dependable Secur. 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