{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:42:11Z","timestamp":1778344931106,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":42,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819932993","type":"print"},{"value":"9789819933006","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-3300-6_21","type":"book-chapter","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T15:04:01Z","timestamp":1685459041000},"page":"296-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Survey of Integrating Federated Learning with Smart Grids: Application Prospect, Privacy Preserving and Challenges Analysis"],"prefix":"10.1007","author":[{"given":"Zhichao","family":"Tang","sequence":"first","affiliation":[]},{"given":"Yan","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Tianhao","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Ruixuan","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Shuyang","family":"Hao","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yizhi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"33520","DOI":"10.1109\/ACCESS.2023.3263547","volume":"11","author":"M Dhinu Lal","year":"2023","unstructured":"Dhinu Lal, M., Varadarajan, R.: A review of machine learning approaches in synchrophasor technology. IEEE Access 11, 33520\u201333541 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3263547","journal-title":"IEEE Access"},{"key":"21_CR2","doi-asserted-by":"publisher","unstructured":"Eddin, M.E., Massaoudi, M., Abu-Rub, H., Shadmand, M., Abdallah, M.: Novel functional community detection in networked smart grid systems-based improved louvain algorithm. In: 2023 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA, pp. 1-6 (2023) https:\/\/doi.org\/10.1109\/TPEC56611.2023.10078573.","DOI":"10.1109\/TPEC56611.2023.10078573"},{"issue":"4","key":"21_CR3","doi-asserted-by":"publisher","first-page":"3637","DOI":"10.1109\/TSG.2021.3066577","volume":"12","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Bennani, I.L., Liu, X., Sun, M., Zhou, Y.: Electricity consumer characteristics identification: a federated learning approach. IEEE Trans. Smart Grid 12(4), 3637\u20133647 (2021)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"2","key":"21_CR4","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1109\/TSG.2020.2965801","volume":"11","author":"Y Xiang","year":"2020","unstructured":"Xiang, Y., et al.: Slope-based shape cluster method for smart metering load profiles. IEEE Trans. Smart Grid 11(2), 1809\u20131811 (2020)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"1","key":"21_CR5","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/TSG.2016.2584581","volume":"8","author":"SM Tabatabaei","year":"2016","unstructured":"Tabatabaei, S.M., Dick, S., Xu, W.: Toward non-intrusive load monitoring via multi-label classification. IEEE Trans. on Smart Grid 8(1), 26\u201340 (2016)","journal-title":"IEEE Trans. on Smart Grid"},{"issue":"9","key":"21_CR6","doi-asserted-by":"publisher","first-page":"4101","DOI":"10.1109\/TII.2018.2832251","volume":"14","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Xu, C., Li, H., Yang, K., Zhou, J., Lin, X.: HealthDep: an efficient and secure deduplication scheme for cloud-assisted eHealth systems. IEEE Trans. Industr. Inf. 14(9), 4101\u20134112 (2018)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Yin, H., Mallya, A., Vahdat, A., Alvarez, J.M., Kautz, J., Molchanov, P.: See through gradients: image batch recovery via gradinversion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16337\u201316346 (2021)","DOI":"10.1109\/CVPR46437.2021.01607"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Melis, L., Song, C., De Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE symposium on security and privacy (SP), pp. 691\u2013706. IEEE (2019) May)","DOI":"10.1109\/SP.2019.00029"},{"issue":"3","key":"21_CR9","doi-asserted-by":"publisher","first-page":"2706","DOI":"10.1109\/TNSE.2021.3074185","volume":"8","author":"L Yin","year":"2021","unstructured":"Yin, L., Feng, J., Xun, H., Sun, Z., Cheng, X.: A privacy-preserving federated learning for multiparty data sharing in social IoTs. IEEE Trans. Netw. Sci. Eng. 8(3), 2706\u20132718 (2021)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"8","key":"21_CR10","doi-asserted-by":"publisher","first-page":"7751","DOI":"10.1109\/JIOT.2020.2991401","volume":"7","author":"Y Liu","year":"2020","unstructured":"Liu, Y., James, J.Q., Kang, J., Niyato, D., Zhang, S.: Privacy-preserving traffic flow prediction: a federated learning approach. IEEE Internet Things J. 7(8), 7751\u20137763 (2020)","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"21_CR11","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1109\/TPDS.2021.3098467","volume":"33","author":"J Mills","year":"2021","unstructured":"Mills, J., Hu, J., Min, G.: Multi-task federated learning for personalised deep neural networks in edge computing. IEEE Trans. Parallel Distrib. Syst. 33(3), 630\u2013641 (2021)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"21_CR12","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1109\/TNSRE.2022.3161272","volume":"30","author":"T Ngo","year":"2022","unstructured":"Ngo, T., et al.: Federated deep learning for the diagnosis of cerebellar ataxia: privacy preservation and auto-crafted feature extractor. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 803\u2013811 (2022)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"2","key":"21_CR13","doi-asserted-by":"publisher","first-page":"1496","DOI":"10.1109\/TSG.2020.3037066","volume":"12","author":"S Bahrami","year":"2020","unstructured":"Bahrami, S., Chen, Y.C., Wong, V.W.: Deep reinforcement learning for demand response in distribution networks. IEEE Trans. Smart Grid 12(2), 1496\u20131506 (2020)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"1","key":"21_CR14","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1109\/TGCN.2022.3140978","volume":"6","author":"O Bouachir","year":"2022","unstructured":"Bouachir, O., Aloqaily, M., \u00d6zkasap, \u00d6., Ali, F.: FederatedGrids: federated learning and blockchain-assisted P2P energy sharing. IEEE Trans. Green Commun. Netw. 6(1), 424\u2013436 (2022)","journal-title":"IEEE Trans. Green Commun. Netw."},{"issue":"3","key":"21_CR15","doi-asserted-by":"publisher","first-page":"2496","DOI":"10.1109\/TPEL.2021.3114671","volume":"37","author":"L Zhao","year":"2021","unstructured":"Zhao, L., Li, J., Li, Q., Li, F.: A federated learning framework for detecting false data injection attacks in solar farms. IEEE Trans. Power Electron. 37(3), 2496\u20132501 (2021)","journal-title":"IEEE Trans. Power Electron."},{"issue":"8","key":"21_CR16","doi-asserted-by":"publisher","first-page":"2803","DOI":"10.1109\/TMC.2020.3045987","volume":"21","author":"YM Saputra","year":"2020","unstructured":"Saputra, Y.M., Nguyen, D., Dinh, H.T., Vu, T.X., Dutkiewicz, E., Chatzinotas, S.: Federated learning meets contract theory: economic-efficiency framework for electric vehicle networks. IEEE Trans. Mob. Comput. 21(8), 2803\u20132817 (2020)","journal-title":"IEEE Trans. Mob. Comput."},{"key":"21_CR17","unstructured":"Federated Reinforcement Learning for Decentralized Voltage Control in Distribution Networks Haotian Liu, Graduate Student Member, IEEE, and Wenchuan Wu, Fellow, IEEE"},{"key":"21_CR18","unstructured":"Level Behind-the-Meter Solar Generation Disaggregation Jun Lin, Jin Ma, Member, IEEE, and Jianguo Zhu, Senior Member, IEE"},{"issue":"2","key":"21_CR19","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TSG.2021.3125677","volume":"13","author":"J Lin","year":"2021","unstructured":"Lin, J., Ma, J., Zhu, J.: Privacy-preserving household characteristic identification with federated learning method. IEEE Trans. Smart Grid 13(2), 1088\u20131099 (2021)","journal-title":"IEEE Trans. Smart Grid"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"\u010cau\u0161evi\u0107, S., et al.: Flexibility prediction in smart grids: making a case for federated learning (2021)","DOI":"10.1049\/icp.2021.2196"},{"issue":"8","key":"21_CR21","doi-asserted-by":"publisher","first-page":"6069","DOI":"10.1109\/JIOT.2021.3110784","volume":"9","author":"M Wen","year":"2021","unstructured":"Wen, M., Xie, R., Lu, K., Wang, L., Zhang, K.: Feddetect: a novel privacy-preserving federated learning framework for energy theft detection in smart grid. IEEE Internet Things J. 9(8), 6069\u20136080 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"21_CR22","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1109\/TII.2021.3095506","volume":"18","author":"Z Su","year":"2021","unstructured":"Su, Z., et al.: Secure and efficient federated learning for smart grid with edge-cloud collaboration. IEEE Trans. Industr. Inf. 18(2), 1333\u20131344 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"5","key":"21_CR23","doi-asserted-by":"publisher","first-page":"2366","DOI":"10.1109\/TSG.2015.2390131","volume":"6","author":"K Akkaya","year":"2015","unstructured":"Akkaya, K., Rabieh, K., Mahmoud, M., Tonyali, S.: Customized certificate revocation lists for IEEE 802.11s-based smart grid AMI networks. IEEE Trans. Smart Grid 6(5), 2366\u20132374 (2015)","journal-title":"IEEE Trans. Smart Grid"},{"issue":"5","key":"21_CR24","doi-asserted-by":"publisher","first-page":"3930","DOI":"10.1109\/JIOT.2021.3100755","volume":"9","author":"SI Popoola","year":"2021","unstructured":"Popoola, S.I., Ande, R., Adebisi, B., Gui, G., Hammoudeh, M., Jogunola, O.: Federated deep learning for zero-day botnet attack detection in IoT-edge devices. IEEE Internet Things J. 9(5), 3930\u20133944 (2021)","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"21_CR25","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1109\/TII.2021.3074915","volume":"18","author":"MB Gough","year":"2021","unstructured":"Gough, M.B., Santos, S.F., Alskaif, T., Javadi, M.S., Castro, R., Catal\u00e3o, J.P.: Preserving privacy of smart meter data in a smart grid environment. IEEE Trans. Industr. Inf. 18(1), 707\u2013718 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"21_CR26","doi-asserted-by":"publisher","unstructured":"Wang, H., Zhang, J., Lu, C., Wu, C.: Privacy preserving in non-intrusive load monitoring: a differential privacy perspective. IEEE Trans. on Smart Grid 12(3), 2529\u20132543 (2021) https:\/\/doi.org\/10.1109\/Tsmartgrid.2020.3038757","DOI":"10.1109\/Tsmartgrid.2020.3038757"},{"key":"21_CR27","doi-asserted-by":"crossref","unstructured":"Duan, M., Liu, D., Chen, X., Liu, R., Tan, Y., Liang, L.: Self-balancing federated learning with global imbalanced data in mobile systems. IEEE Trans. Parallel Distrib. Syst. 32(1), 59\u201371 (2021)","DOI":"10.1109\/TPDS.2020.3009406"},{"issue":"6","key":"21_CR28","doi-asserted-by":"publisher","first-page":"4049","DOI":"10.1109\/TII.2021.3085960","volume":"18","author":"B Jia","year":"2021","unstructured":"Jia, B., Zhang, X., Liu, J., Zhang, Y., Huang, K., Liang, Y.: Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in IIoT. IEEE Trans. Industr. Inf. 18(6), 4049\u20134058 (2021)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Bai, Y., Fan, M.: A method to improve the privacy and security for federated learning. In: 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS), pp. 704\u2013708. IEEE (2021) April)","DOI":"10.1109\/ICCCS52626.2021.9449214"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Sun, Y., Shao, J., Mao, Y., Wang, J.H., Zhang, J.: Semi-decentralized federated edge learning for fast convergence on non-IID data. In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1898\u20131903. IEEE (2022) April","DOI":"10.1109\/WCNC51071.2022.9771904"},{"key":"21_CR31","unstructured":"Ammad-ud-din, M., Ivannikova, E., Khan, S.A., Oyomno, W., Fu, Q., Tan, K.E., et al.: Federated collaborative filtering for privacy-preserving personalized recommendation system (2019)"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Luo, M.Y., Lin, S.W.: From monolithic systems to a federated e-learning cloud system. In: 2013 IEEE international conference on cloud engineering (IC2E), pp. 156\u2013165. IEEE (2013) March","DOI":"10.1109\/IC2E.2013.39"},{"key":"21_CR33","doi-asserted-by":"publisher","unstructured":"Shen, Z., Wu, Q., Qian, J., Gu, C., Sun, F., Tan, J.: Federated learning for long-term forecasting of electricity consumption towards a carbon-neutral future. In: 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 789\u2013793 (2022) https:\/\/doi.org\/10.1109\/ICSP54964.2022.9778813","DOI":"10.1109\/ICSP54964.2022.9778813"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Wang, X., Liang, X., Zheng, X., An, N.: Electricity federated strategies based on restricted solution space. In: 2021 6th International Conference on Communication, Image and Signal Processing (CCISP), pp. 329\u2013333. IEEE (2021) November","DOI":"10.1109\/CCISP52774.2021.9639260"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Li, F.Q., Wang, S.L., Liew, A.W.C.: Watermarking protocol for deep neural network ownership regulation in federated learning. In: 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1\u20134. IEEE (2022) July","DOI":"10.1109\/ICMEW56448.2022.9859395"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Freitag, F., Vilchez, P., Wei, L., Liu, C.H., Selimi, M., Koutsopoulos, I.: An experimental environment based on mini-pcs for federated learning research. In: 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), pp. 927\u2013928. IEEE (2022) January","DOI":"10.1109\/CCNC49033.2022.9700579"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Cui, J., Wu, Q., Zhou, Z., Chen, X.: FedBranch: heterogeneous federated learning via multi-branch neural network. In: 2022 IEEE\/CIC International Conference on Communications in China (ICCC), pp. 1101\u20131106. IEEE (2022) August","DOI":"10.1109\/ICCC55456.2022.9880769"},{"key":"21_CR38","doi-asserted-by":"crossref","unstructured":"Kim, H., Kim, Y., Park, H.: Reducing model cost based on the weights of each layer for federated learning clustering. In: 2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 405\u2013408. IEEE (2021) August","DOI":"10.1109\/ICUFN49451.2021.9528575"},{"key":"21_CR39","doi-asserted-by":"crossref","unstructured":"Yang, B., Cao, X., Bassey, J., Li, X., Qian, L.: Computation offloading in multi-access edge computing: a multi-task learning approach. In: Proceedings of the IEEE International Conference on Communications, pp. 1\u20136 (2019)","DOI":"10.1109\/ICC.2019.8761212"},{"key":"21_CR40","doi-asserted-by":"crossref","unstructured":"Khan, L.U., Alsenwi, M., Han, Z., Hong, C.S.: Self organizing federated learning over wireless networks: a socially aware clustering approach. In: 2020 international conference on information networking (ICOIN), pp. 453\u2013458. IEEE (2020) January","DOI":"10.1109\/ICOIN48656.2020.9016505"},{"key":"21_CR41","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Shu, J., Jia, X., Huang, H.: Clustered federated multi-task learning with NON-IID data. In: 2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS), pp. 50\u201357. IEEE (2021) December","DOI":"10.1109\/ICPADS53394.2021.00012"},{"key":"21_CR42","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, W., Li, B.: CMFL: mitigating communication overhead for federated learning. In: Proceedings of the 39th IEEE International Conference on Distributed Computing Systems, pp. 954\u2013964 (2019)","DOI":"10.1109\/ICDCS.2019.00099"}],"container-title":["Communications in Computer and Information Science","Big Data and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-3300-6_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T13:47:56Z","timestamp":1729518476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-3300-6_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819932993","9789819933006"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-3300-6_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICBDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbds2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icbds.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}