{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T22:33:07Z","timestamp":1770762787749,"version":"3.50.0"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032172853","type":"print"},{"value":"9783032172860","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-17286-0_6","type":"book-chapter","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:56:43Z","timestamp":1770753403000},"page":"124-145","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Edge AI Collaborative Learning: Bayesian Approaches to\u00a0Uncertainty Estimation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7145-5630","authenticated-orcid":false,"given":"Gleb","family":"Radchenko","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6289-0098","authenticated-orcid":false,"given":"Victoria Andrea","family":"Fill","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,11]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","unstructured":"Abreha, H.G., Hayajneh, M., Serhani, M.A.: Federated learning in edge computing: a systematic survey (2022). https:\/\/doi.org\/10.3390\/s22020450","DOI":"10.3390\/s22020450"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Ahn, J.H., Simeone, O., Kang, J.: Wireless federated distillation for distributed edge learning with heterogeneous data, vol. 2019-September (2019). https:\/\/doi.org\/10.1109\/PIMRC.2019.8904164","DOI":"10.1109\/PIMRC.2019.8904164"},{"key":"6_CR3","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1348\/000711010X522227","volume":"64","author":"DI Belov","year":"2011","unstructured":"Belov, D.I., Armstrong, R.D.: Distributions of the Kullback-Leibler divergence with applications. Br. J. Math. Stat. Psychol. 64, 291\u2013309 (2011). https:\/\/doi.org\/10.1348\/000711010X522227","journal-title":"Br. J. Math. Stat. Psychol."},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"3249","DOI":"10.1109\/TMI.2021.3077857","volume":"40","author":"S Bhadra","year":"2021","unstructured":"Bhadra, S., Kelkar, V.A., Brooks, F.J., Anastasio, M.A.: On hallucinations in tomographic image reconstruction. IEEE Trans. Med. Imaging 40, 3249\u20133260 (2021). https:\/\/doi.org\/10.1109\/TMI.2021.3077857","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR5","doi-asserted-by":"publisher","unstructured":"Boyd, S.: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, vol.\u00a03 (2010). https:\/\/doi.org\/10.1561\/2200000016","DOI":"10.1561\/2200000016"},{"key":"6_CR6","unstructured":"Claici, S., Yurochkin, M., Ghosh, S., Solomon, J.: Model fusion with Kullback-Leibler divergence, vol. PartF168147-3 (2020)"},{"key":"6_CR7","doi-asserted-by":"publisher","unstructured":"D, S.: Kernel Density Estimators, pp. 137\u2013216 (2015). https:\/\/doi.org\/10.1002\/9781118575574.ch6. https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/9781118575574.ch6","DOI":"10.1002\/9781118575574.ch6"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Elgabli, A., Park, J., Bedi, A.S., Bennis, M., Aggarwal, V.: Gadmm: fast and communication efficient framework for distributed machine learning. J. Mach. Learn. Res. 21 (2020)","DOI":"10.1109\/CISS48834.2020.1570627384"},{"key":"6_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2018.05.003","volume":"116","author":"O Gupta","year":"2018","unstructured":"Gupta, O., Raskar, R.: Distributed learning of deep neural network over multiple agents. J. Netw. Comput. Appl. 116, 1\u20138 (2018). https:\/\/doi.org\/10.1016\/j.jnca.2018.05.003","journal-title":"J. Netw. Comput. Appl."},{"key":"6_CR10","doi-asserted-by":"publisher","first-page":"121046","DOI":"10.1109\/ACCESS.2021.3108455","volume":"9","author":"YJ Ha","year":"2021","unstructured":"Ha, Y.J., et al.: Spatio-temporal split learning for privacy-preserving medical platforms: case studies with covid-19 CT, X-ray, and cholesterol data. IEEE Access 9, 121046\u2013121059 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3108455","journal-title":"IEEE Access"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"9041","DOI":"10.1109\/TWC.2022.3172147","volume":"21","author":"Z Jiang","year":"2022","unstructured":"Jiang, Z., Yu, G., Cai, Y., Jiang, Y.: Decentralized edge learning via unreliable device-to-device communications. IEEE Trans. Wirel. Commun. 21, 9041\u20139055 (2022). https:\/\/doi.org\/10.1109\/TWC.2022.3172147","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/MCI.2022.3155327","volume":"17","author":"LV Jospin","year":"2022","unstructured":"Jospin, L.V., Laga, H., Boussaid, F., Buntine, W., Bennamoun, M.: Hands-on Bayesian neural networks\u2013a tutorial for deep learning users. IEEE Comput. Intell. Mag. 17, 29\u201348 (2022). https:\/\/doi.org\/10.1109\/MCI.2022.3155327","journal-title":"IEEE Comput. Intell. Mag."},{"key":"6_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/978-3-030-20205-7_3","volume-title":"Image Analysis","author":"A Kalervo","year":"2019","unstructured":"Kalervo, A., Ylioinas, J., H\u00e4iki\u00f6, M., Karhu, A., Kannala, J.: CubiCasa5K: a dataset and an improved multi-task model for floorplan image analysis. In: Felsberg, M., Forss\u00e9n, P.-E., Sintorn, I.-M., Unger, J. (eds.) SCIA 2019. LNCS, vol. 11482, pp. 28\u201340. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20205-7_3"},{"key":"6_CR14","doi-asserted-by":"publisher","unstructured":"Koda, Y., et al.: Communication-efficient multimodal split learning for mmwave received power prediction. IEEE Commun. Lett. 24 (2020). https:\/\/doi.org\/10.1109\/LCOMM.2020.2978824","DOI":"10.1109\/LCOMM.2020.2978824"},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22, 79\u201386 (1951). https:\/\/doi.org\/10.1214\/aoms\/1177729694","journal-title":"Ann. Math. Stat."},{"key":"6_CR16","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1109\/TWC.2019.2946140","volume":"19","author":"E Li","year":"2020","unstructured":"Li, E., Zeng, L., Zhou, Z., Chen, X.: Edge AI: on-demand accelerating deep neural network inference via edge computing. IEEE Trans. Wirel. Commun. 19, 447\u2013457 (2020). https:\/\/doi.org\/10.1109\/TWC.2019.2946140","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3582080","volume":"18","author":"Q Liang","year":"2023","unstructured":"Liang, Q., Hanafy, W.A., Ali-Eldin, A., Shenoy, P.: Model-driven cluster resource management for AI workloads in edge clouds. ACM Trans. Auton. Adaptive Syst. 18, 1\u201326 (2023). https:\/\/doi.org\/10.1145\/3582080","journal-title":"ACM Trans. Auton. Adaptive Syst."},{"key":"6_CR18","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","volume":"22","author":"WYB Lim","year":"2020","unstructured":"Lim, W.Y.B., et al.: Federated learning in mobile edge networks: a comprehensive survey. IEEE Commun. Surv. Tutor. 22, 2031\u20132063 (2020). https:\/\/doi.org\/10.1109\/COMST.2020.2986024","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"6_CR19","doi-asserted-by":"publisher","first-page":"8726","DOI":"10.1109\/TNNLS.2022.3216981","volume":"35","author":"L Lyu","year":"2024","unstructured":"Lyu, L., et al.: Privacy and robustness in federated learning: attacks and defenses. IEEE Trans. Neural Netw. Learn. Syst. 35, 8726\u20138746 (2024). https:\/\/doi.org\/10.1109\/TNNLS.2022.3216981","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"6_CR20","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 (2017)"},{"key":"6_CR21","doi-asserted-by":"publisher","unstructured":"Melis, L., Song, C., Cristofaro, E.D., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 691\u2013706. IEEE (2019). https:\/\/doi.org\/10.1109\/SP.2019.00029","DOI":"10.1109\/SP.2019.00029"},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.3390\/s20092533","volume":"20","author":"M Merenda","year":"2020","unstructured":"Merenda, M., Porcaro, C., Iero, D.: Edge machine learning for AI-enabled IoT devices: a review. Sensors 20, 2533 (2020). https:\/\/doi.org\/10.3390\/s20092533","journal-title":"Sensors"},{"key":"6_CR23","doi-asserted-by":"publisher","unstructured":"Nguyen, V.D., Chatzinotas, S., Ottersten, B., Duong, T.Q.: Fedfog: Network-aware optimization of federated learning over wireless fog-cloud systems. IEEE Trans. Wirel. Commun. 21 (2022). https:\/\/doi.org\/10.1109\/TWC.2022.3167263","DOI":"10.1109\/TWC.2022.3167263"},{"key":"6_CR24","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1109\/JPROC.2021.3055679","volume":"109","author":"J Park","year":"2021","unstructured":"Park, J., et al.: Communication-efficient and distributed learning over wireless networks: principles and applications. Proc. IEEE 109, 796\u2013819 (2021). https:\/\/doi.org\/10.1109\/JPROC.2021.3055679","journal-title":"Proc. IEEE"},{"key":"6_CR25","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/MM.2023.3321249","volume":"43","author":"V Parmar","year":"2023","unstructured":"Parmar, V., Sarwar, S.S., Li, Z., Lee, H.H.S., Salvo, B.D., Suri, M.: Exploring memory-oriented design optimization of edge AI hardware for extended reality applications. IEEE Micro 43, 40\u201349 (2023). https:\/\/doi.org\/10.1109\/MM.2023.3321249","journal-title":"IEEE Micro"},{"key":"6_CR26","doi-asserted-by":"publisher","unstructured":"Radchenko, G., Fill, V.: Uncertainty estimation in multi-agent distributed learning for AI-enabled edge devices. In: Proceedings of the 14th International Conference on Cloud Computing and Services Science, pp. 311\u2013318. SCITEPRESS - Science and Technology Publications (2024). https:\/\/doi.org\/10.5220\/0012728500003711","DOI":"10.5220\/0012728500003711"},{"key":"6_CR27","doi-asserted-by":"publisher","unstructured":"Samie, F., Tsoutsouras, V., Bauer, L., Xydis, S., Soudris, D., Henkel, J.: Computation offloading and resource allocation for low-power IoT edge devices, pp. 7\u201312. IEEE (2016). https:\/\/doi.org\/10.1109\/WF-IoT.2016.7845499","DOI":"10.1109\/WF-IoT.2016.7845499"},{"key":"6_CR28","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1109\/TNSE.2021.3081748","volume":"9","author":"F Sattler","year":"2022","unstructured":"Sattler, F., Marban, A., Rischke, R., Samek, W.: CFD: communication-efficient federated distillation via soft-label quantization and delta coding. IEEE Trans. Netw. Sci. Eng. 9, 2025\u20132038 (2022). https:\/\/doi.org\/10.1109\/TNSE.2021.3081748","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"6_CR29","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1038\/s41467-023-44383-9","volume":"15","author":"J Shao","year":"2024","unstructured":"Shao, J., Wu, F., Zhang, J.: Selective knowledge sharing for privacy-preserving federated distillation without a good teacher. Nat. Commun. 15, 349 (2024). https:\/\/doi.org\/10.1038\/s41467-023-44383-9","journal-title":"Nat. Commun."},{"key":"6_CR30","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MCOM.001.2000373","volume":"58","author":"J Shao","year":"2020","unstructured":"Shao, J., Zhang, J.: Communication-computation trade-off in resource-constrained edge inference. IEEE Commun. Mag. 58, 20\u201326 (2020). https:\/\/doi.org\/10.1109\/MCOM.001.2000373","journal-title":"IEEE Commun. Mag."},{"key":"6_CR31","unstructured":"Sitzmann, V., Martel, J.N., Bergman, A.W., Lindell, D.B., Wetzstein, G.: Implicit neural representations with periodic activation functions. In: Advances in Neural Information Processing Systems, vol. 2020-December (2020)"},{"key":"6_CR32","doi-asserted-by":"publisher","unstructured":"Sudharsan, B., Breslin, J.G., Ali, M.I.: Edge2train: a framework to train machine learning models (SVMs) on resource-constrained IoT edge devices. In: Proceedings of the 10th International Conference on the Internet of Things, pp.\u00a01\u20138. ACM (2020). https:\/\/doi.org\/10.1145\/3410992.3411014","DOI":"10.1145\/3410992.3411014"},{"key":"6_CR33","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/MNET.011.2000478","volume":"35","author":"A Tak","year":"2021","unstructured":"Tak, A., Cherkaoui, S.: Federated edge learning: design issues and challenges. IEEE Netw. 35, 252\u2013258 (2021). https:\/\/doi.org\/10.1109\/MNET.011.2000478","journal-title":"IEEE Netw."},{"key":"6_CR34","unstructured":"Vepakomma, P., Gupta, O., Swedish, T., Raskar, R.: Split learning for health: distributed deep learning without sharing raw patient data. arXiv preprint arXiv:1812.00564 (2018)"},{"key":"6_CR35","doi-asserted-by":"publisher","unstructured":"Wang, B., Dong, K., Zakaria, N.A.B., Upadhyay, M., Wong, W.F., Peh, L.S.: Network-on-chip-centric accelerator architectures for edge AI computing, pp. 243\u2013244. IEEE (2022). https:\/\/doi.org\/10.1109\/ISOCC56007.2022.10031356","DOI":"10.1109\/ISOCC56007.2022.10031356"},{"key":"6_CR36","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.1038\/s41467-022-29763-x","volume":"13","author":"C Wu","year":"2022","unstructured":"Wu, C., Wu, F., Lyu, L., Huang, Y., Xie, X.: Communication-efficient federated learning via knowledge distillation. Nat. Commun. 13, 2032 (2022). https:\/\/doi.org\/10.1038\/s41467-022-29763-x","journal-title":"Nat. Commun."},{"key":"6_CR37","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.1109\/JSAC.2023.3242704","volume":"41","author":"W Wu","year":"2023","unstructured":"Wu, W., et al.: Split learning over wireless networks: parallel design and resource management. IEEE J. Sel. Areas Commun. 41, 1051\u20131066 (2023). https:\/\/doi.org\/10.1109\/JSAC.2023.3242704","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"6_CR38","doi-asserted-by":"publisher","unstructured":"Yang, K., et al.: Spatio-temporal domain awareness for multi-agent collaborative perception. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 23326\u201323335. IEEE (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.02137","DOI":"10.1109\/ICCV51070.2023.02137"},{"key":"6_CR39","doi-asserted-by":"publisher","first-page":"1896","DOI":"10.1109\/LRA.2022.3142402","volume":"7","author":"J Yu","year":"2022","unstructured":"Yu, J., Vincent, J.A., Schwager, M.: Dinno: distributed neural network optimization for multi-robot collaborative learning. IEEE Robot. Autom. Lett. 7, 1896\u20131903 (2022). https:\/\/doi.org\/10.1109\/LRA.2022.3142402","journal-title":"IEEE Robot. Autom. Lett."},{"key":"6_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3464419","volume":"54","author":"J Zhang","year":"2022","unstructured":"Zhang, J., et al.: Edge learning: the enabling technology for distributed big data analytics in the edge. ACM Comput. Surv. 54, 1\u201336 (2022). https:\/\/doi.org\/10.1145\/3464419","journal-title":"ACM Comput. Surv."}],"container-title":["Communications in Computer and Information Science","Cloud Computing and Services Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-17286-0_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:56:45Z","timestamp":1770753405000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-17286-0_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032172853","9783032172860"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-17286-0_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"11 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CLOSER","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cloud Computing and Services Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Angers","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"2 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"closer2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/closer.scitevents.org\/?y=2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}