{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:22:32Z","timestamp":1770290552932,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T00:00:00Z","timestamp":1743292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Abdullah University of Science and Technology Research Funding (KRF)","award":["ORA-CRG2021-4699"],"award-info":[{"award-number":["ORA-CRG2021-4699"]}]},{"name":"Slovenian Research Agency","award":["J2-3047"],"award-info":[{"award-number":["J2-3047"]}]},{"name":"Slovenian Research Agency","award":["N2-0393"],"award-info":[{"award-number":["N2-0393"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,3,30]]},"DOI":"10.1145\/3721146.3721936","type":"proceedings-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T17:42:05Z","timestamp":1743529325000},"page":"183-191","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards a Unified Framework for Split Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4142-931X","authenticated-orcid":false,"given":"Boris","family":"Radovi\u010d","sequence":"first","affiliation":[{"name":"KAUST, Thuwal, Saudi Arabia"},{"name":"University of Ljubljana, Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5051-4283","authenticated-orcid":false,"given":"Marco","family":"Canini","sequence":"additional","affiliation":[{"name":"KAUST, Thuwal, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0619-9260","authenticated-orcid":false,"given":"Samuel","family":"Horv\u00e1th","sequence":"additional","affiliation":[{"name":"MBZUAI, Abu Dhabi, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9009-0024","authenticated-orcid":false,"given":"Veljko","family":"Pejovi\u0107","sequence":"additional","affiliation":[{"name":"University of Ljubljana, Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2296-9296","authenticated-orcid":false,"given":"Praneeth","family":"Vepakomma","sequence":"additional","affiliation":[{"name":"MBZUAI, Abu Dhabi, United Arab Emirates"},{"name":"MIT, Cambridge, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Sharif Abuadbba Kyuyeon Kim Minki Kim Chandra Thapa Seyit Ahmet \u00c7amtepe Yansong Gao Hyoungshick Kim and Surya Nepal. 2020. Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?. In ASIA CCS.","DOI":"10.1145\/3320269.3384740"},{"key":"e_1_3_2_1_2_1","volume-title":"Improving the Communication and Computation Efficiency of Split Learning for IoT Applications. In IEEE Global Communications Conference, GLOBECOM","author":"Ayad Ahmad","year":"2021","unstructured":"Ahmad Ayad, Melvin Renner, and Anke Schmeink. 2021. Improving the Communication and Computation Efficiency of Split Learning for IoT Applications. In IEEE Global Communications Conference, GLOBECOM 2021."},{"key":"e_1_3_2_1_3_1","volume-title":"Lane","author":"Beutel Daniel J.","year":"2020","unstructured":"Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D. Lane. 2020. Flower: A Friendly Federated Learning Research Framework. (2020)."},{"key":"e_1_3_2_1_4_1","volume-title":"Petals: Collaborative Inference and Fine-tuning of Large Models. In ACL.","author":"Borzunov Alexander","year":"2023","unstructured":"Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, and Colin Raffel. 2023. Petals: Collaborative Inference and Fine-tuning of Large Models. In ACL."},{"key":"e_1_3_2_1_5_1","volume-title":"Where is the Testbed for my Federated Learning Research?","author":"Bo\u017ei\u010d Janez","year":"2024","unstructured":"Janez Bo\u017ei\u010d, Amandio R Faustino, Boris Radovi\u010d, Marco Canini, and Veljko Pejovi\u0107. 2024. Where is the Testbed for my Federated Learning Research? (2024)."},{"key":"e_1_3_2_1_6_1","volume-title":"Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, and Ramesh Raskar.","author":"Chopra Ayush","year":"2021","unstructured":"Ayush Chopra, Surya Kant Sahu, Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, and Ramesh Raskar. 2021. AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning. (2021)."},{"key":"e_1_3_2_1_7_1","unstructured":"Enmao Diao Jie Ding and Vahid Tarokh. 2021. HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. In ICLR."},{"key":"e_1_3_2_1_8_1","unstructured":"Tao Fan Yan Kang Guoqiang Ma Weijing Chen Wenbin Wei Lixin Fan and Qiang Yang. 2023. FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models. (2023)."},{"key":"e_1_3_2_1_9_1","volume-title":"MADRL-based model partitioning, aggregation control, and resource allocation for cloud-edge-device collaborative split federated learning","author":"Fan Wenhao","year":"2025","unstructured":"Wenhao Fan, Penghui Chen, Xiongfei Chun, and Yuan'an Liu. 2025. MADRL-based model partitioning, aggregation control, and resource allocation for cloud-edge-device collaborative split federated learning. IEEE Trans. Mob. Comput. (2025)."},{"key":"e_1_3_2_1_10_1","volume-title":"https:\/\/flower.ai\/blog\/2024-03-14-introducing-flowerllm\/. Accessed","author":"Introducing","year":"2025","unstructured":"Flower. 2024. Introducing FlowerLLM. https:\/\/flower.ai\/blog\/2024-03-14-introducing-flowerllm\/. Accessed: 2 Feb 2025."},{"key":"e_1_3_2_1_11_1","volume-title":"End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things. In International Symposium on Reliable Distributed Systems, SRDS","author":"Gao Yansong","year":"2020","unstructured":"Yansong Gao, Minki Kim, Sharif Abuadbba, Yeonjae Kim, Chandra Thapa, Kyuyeon Kim, Seyit Ahmet \u00c7amtepe, Hyoungshick Kim, and Surya Nepal. 2020. End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things. In International Symposium on Reliable Distributed Systems, SRDS 2020."},{"key":"e_1_3_2_1_12_1","volume-title":"Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things","author":"Gao Yansong","year":"2022","unstructured":"Yansong Gao, Minki Kim, Chandra Thapa, Alsharif Abuadbba, Zhi Zhang, Seyit Camtepe, Hyoungshick Kim, and Surya Nepal. 2022. Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things. IEEE Trans. Computers (2022)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Manish Gawali C. S. Arvind Shriya Suryavanshi Harshit Madaan Ashrika Gaikwad K. N. Bhanu Prakash Viraj Kulkarni and Aniruddha Pant. 2021. Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcare. In MIUA.","DOI":"10.1007\/978-3-030-80432-9_34"},{"key":"e_1_3_2_1_14_1","volume-title":"Performance Best Practices. https:\/\/grpc.io\/docs\/guides\/performance. Accessed","author":"RPC","year":"2025","unstructured":"gRPC documentation. 2025. Performance Best Practices. https:\/\/grpc.io\/docs\/guides\/performance. Accessed: 27 Jan 2025."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Otkrist Gupta and Ramesh Raskar. 2018. Distributed learning of deep neural network over multiple agents. J. Netw. Comput. Appl. (2018).","DOI":"10.1016\/j.jnca.2018.05.003"},{"key":"e_1_3_2_1_16_1","unstructured":"Dong-Jun Han Hasnain Irshad Bhatti Jungmoon Lee and Jaekyun Moon. [n. d.]. Accelerating federated learning with split learning on locally generated losses. https:\/\/fl-icml.github.io\/2021\/papers\/FL-ICML21\\_paper\\_6.pdf. Accessed: 2023-11-16."},{"key":"e_1_3_2_1_17_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR."},{"key":"e_1_3_2_1_18_1","volume-title":"Lane","author":"Horv\u00e1th Samuel","year":"2021","unstructured":"Samuel Horv\u00e1th, Stefanos Laskaridis, M\u00e1rio Almeida, Ilias Leontiadis, Stylianos I. Venieris, and Nicholas D. Lane. 2021. FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. In NeurIPS."},{"key":"e_1_3_2_1_19_1","volume-title":"Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training With Non-IID Private Data","author":"Itahara Sohei","year":"2023","unstructured":"Sohei Itahara, Takayuki Nishio, Yusuke Koda, Masahiro Morikura, and Koji Yamamoto. 2023. Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training With Non-IID Private Data. IEEE Trans. Mob. Comput. (2023)."},{"key":"e_1_3_2_1_20_1","volume-title":"Arjun Nitin and Bonawitz, Kallista and Charles, Zachary and Cormode, Graham and Cummings, Rachel and others","author":"McMahan Peter","year":"2021","unstructured":"Kairouz, Peter and McMahan, H Brendan and Avent, Brendan and Bellet, Aur\u00e9lien and Bennis, Mehdi and Bhagoji, Arjun Nitin and Bonawitz, Kallista and Charles, Zachary and Cormode, Graham and Cummings, Rachel and others. 2021. Advances and Open Problems in Federated Learning. Foundations and Trends\u00ae in Machine Learning (2021)."},{"key":"e_1_3_2_1_21_1","unstructured":"Mohammad Kohankhaki Ahmad Ayad Mahdi Barhoush and Anke Schmeink. 2024. Parallel Split Learning with Global Sampling. (2024)."},{"key":"e_1_3_2_1_22_1","volume-title":"Ananda Theertha Suresh, and Dave Bacon","author":"Kone\u010dn\u00fd Jakub","year":"2016","unstructured":"Jakub Kone\u010dn\u00fd, H. Brendan McMahan, Felix X. Yu, Peter Richt\u00e1rik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated Learning: Strategies for Improving Communication Efficiency. (2016)."},{"key":"e_1_3_2_1_23_1","unstructured":"Daliang Li and Junpu Wang. 2019. FedMD: Heterogenous Federated Learning via Model Distillation. (2019)."},{"key":"e_1_3_2_1_24_1","unstructured":"Tao Lin Lingjing Kong Sebastian U. Stich and Martin Jaggi. 2020. Ensemble Distillation for Robust Model Fusion in Federated Learning. In NeurIPS."},{"key":"e_1_3_2_1_25_1","volume-title":"Letaief","author":"Liu Lumin","year":"2020","unstructured":"Lumin Liu, Jun Zhang, Shenghui Song, and Khaled B. Letaief. 2020. Client-Edge-Cloud Hierarchical Federated Learning. In ICC."},{"key":"e_1_3_2_1_26_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Ag\u00fcera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Deepak Narayanan Aaron Harlap Amar Phanishayee Vivek Seshadri Nikhil R. Devanur Gregory R. Ganger Phillip B. Gibbons and Matei Zaharia. 2019. PipeDream: generalized pipeline parallelism for DNN training. In SOSP.","DOI":"10.1145\/3341301.3359646"},{"key":"e_1_3_2_1_28_1","unstructured":"Seungeun Oh Jihong Park Praneeth Vepakomma Sihun Baek Ramesh Raskar Mehdi Bennis and Seong-Lyun Kim. 2022. LocFedMix-SL: Localize Federate and Mix for Improved Scalability Convergence and Latency in Split Learning. In WWW."},{"key":"e_1_3_2_1_29_1","volume-title":"Roberto Gon\u00e7alves Pacheco, and Rodrigo S. Couto","author":"Haseena Rahmath","year":"2025","unstructured":"Haseena Rahmath P, Vishal Srivastava, Kuldeep Chaurasia, Roberto Gon\u00e7alves Pacheco, and Rodrigo S. Couto. 2025. Early-Exit Deep Neural Network - A Comprehensive Survey. ACM Comput. Surv. (2025)."},{"key":"e_1_3_2_1_30_1","unstructured":"Shraman Pal Mansi Uniyal Jihong Park Praneeth Vepakomma Ramesh Raskar Mehdi Bennis Moongu Jeon and Jinho Choi. 2021. Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning. (2021)."},{"key":"e_1_3_2_1_31_1","unstructured":"Maarten G. Poirot Praneeth Vepakomma Ken Chang Jayashree Kalpathy-Cramer Rajiv Gupta and Ramesh Raskar. 2019. Split Learning for collaborative deep learning in healthcare. (2019)."},{"key":"e_1_3_2_1_32_1","unstructured":"Boris Radovi\u010d Mohammed Aljahdali Marco Canini Veljko Pejovi\u0107 and Zuhair Khayyat. 2024. Train your cake and eat it too! Repurposing collaborative training to tailor LLMs to private data without sharing. (2024)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Boris Radovi\u010d Marco Canini and Veljko Pejovi\u0107. 2024. Review and comparative evaluation of resource-adaptive collaborative training for heterogeneous edge devices. ACM Trans. Model. Perform. Eval. Comput. Syst. (2024).","DOI":"10.1145\/3708983"},{"key":"e_1_3_2_1_34_1","unstructured":"Sashank J. Reddi Zachary Charles Manzil Zaheer Zachary Garrett Keith Rush Jakub Kone\u010dn\u00fd Sanjiv Kumar and Hugh Brendan McMahan. 2021. Adaptive Federated Optimization. In ICLR."},{"key":"e_1_3_2_1_35_1","volume-title":"Antonio Di Maio, and Torsten Braun","author":"Samikwa Eric","year":"2022","unstructured":"Eric Samikwa, Antonio Di Maio, and Torsten Braun. 2022. ARES: Adaptive Resource-Aware Split Learning for Internet of Things. Comput. Networks (2022)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"Yue Tan Guodong Long Lu Liu Tianyi Zhou Qinghua Lu Jing Jiang and Chengqi Zhang. 2022. FedProto: Federated Prototype Learning across Heterogeneous Clients. In AAAI.","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"e_1_3_2_1_37_1","volume-title":"Bo Li, Bingsheng He, and Xiaowen Chu.","author":"Tang Zhenheng","year":"2024","unstructured":"Zhenheng Tang, Xueze Kang, Yiming Yin, Xinglin Pan, Yuxin Wang, Xin He, Qiang Wang, Rongfei Zeng, Kaiyong Zhao, Shaohuai Shi, Amelie Chi Zhou, Bo Li, Bingsheng He, and Xiaowen Chu. 2024. FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression. (2024)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Surat Teerapittayanon Bradley McDanel and H. T. Kung. 2016. BranchyNet: Fast inference via early exiting from deep neural networks. In ICPR.","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"e_1_3_2_1_39_1","volume-title":"Seyit Camtepe, and Lichao Sun.","author":"Thapa Chandra","year":"2022","unstructured":"Chandra Thapa, Mahawaga Arachchige Pathum Chamikara, Seyit Camtepe, and Lichao Sun. 2022. SplitFed: When Federated Learning Meets Split Learning. In AAAI."},{"key":"e_1_3_2_1_40_1","unstructured":"Praneeth Vepakomma Otkrist Gupta Tristan Swedish and Ramesh Raskar. 2018. Split learning for health: Distributed deep learning without sharing raw patient data. (2018)."},{"key":"e_1_3_2_1_41_1","unstructured":"Pablo Villalobos Anson Ho Jaime Sevilla Tamay Besiroglu Lennart Heim and Marius Hobbhahn. 2024. Will we run out of data? Limits of LLM scaling based on human-generated data. (2024)."},{"key":"e_1_3_2_1_42_1","unstructured":"Pablo Villalobos Jaime Sevilla Tamay Besiroglu Lennart Heim Anson Ho and Marius Hobbhahn. 2022. Machine Learning Model Sizes and the Parameter Gap. (2022)."},{"key":"e_1_3_2_1_43_1","unstructured":"Chuhan Wu Fangzhao Wu Lingjuan Lyu Yongfeng Huang and Xing Xie. 2022. Communication-efficient federated learning via knowledge distillation. Nat. Commun. (2022)."}],"event":{"name":"EuroMLSys '25: 5th Workshop on Machine Learning and Systems","location":"World Trade Center Rotterdam Netherlands","acronym":"EuroMLSys '25","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the 5th Workshop on Machine Learning and Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3721146.3721936","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3721146.3721936","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:57:39Z","timestamp":1750298259000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3721146.3721936"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,30]]},"references-count":43,"alternative-id":["10.1145\/3721146.3721936","10.1145\/3721146"],"URL":"https:\/\/doi.org\/10.1145\/3721146.3721936","relation":{},"subject":[],"published":{"date-parts":[[2025,3,30]]},"assertion":[{"value":"2025-04-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}