[{"year":"2023","external_id":{"arxiv":["2302.02390"]},"ec_funded":1,"acknowledgement":"The authors gratefully acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), as well as experimental support from the IST Austria IT department, in particular Stefano Elefante, Andrei Hornoiu, and Alois Schloegl. AV acknowledges the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-21-CE48-0016 (project COMCOPT), the support of Fondation Hadamard with a PRMO grant, and the support of CNRS with a CoopIntEER IEA grant (project ALFRED).","date_published":"2023-07-30T00:00:00Z","conference":{"location":"Honolulu, Hawaii, HI, United States","name":"ICML: International Conference on Machine Learning","start_date":"2023-07-23","end_date":"2023-07-29"},"status":"public","publication":"Proceedings of the 40th International Conference on Machine Learning","project":[{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning"}],"_id":"14461","date_updated":"2023-10-31T09:40:45Z","type":"conference","article_processing_charge":"No","alternative_title":["PMLR"],"publisher":"ML Research Press","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2302.02390"}],"quality_controlled":"1","page":"24020-24044","department":[{"_id":"DaAl"}],"month":"07","arxiv":1,"citation":{"ama":"Markov I, Vladu A, Guo Q, Alistarh D-A. Quantized distributed training of large models with convergence guarantees. In: <i>Proceedings of the 40th International Conference on Machine Learning</i>. Vol 202. ML Research Press; 2023:24020-24044.","short":"I. Markov, A. Vladu, Q. Guo, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 24020–24044.","ieee":"I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in <i>Proceedings of the 40th International Conference on Machine Learning</i>, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.","chicago":"Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized Distributed Training of Large Models with Convergence Guarantees.” In <i>Proceedings of the 40th International Conference on Machine Learning</i>, 202:24020–44. ML Research Press, 2023.","ista":"Markov I, Vladu A, Guo Q, Alistarh D-A. 2023. Quantized distributed training of large models with convergence guarantees. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 24020–24044.","mla":"Markov, Ilia, et al. “Quantized Distributed Training of Large Models with Convergence Guarantees.” <i>Proceedings of the 40th International Conference on Machine Learning</i>, vol. 202, ML Research Press, 2023, pp. 24020–44.","apa":"Markov, I., Vladu, A., Guo, Q., &#38; Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In <i>Proceedings of the 40th International Conference on Machine Learning</i> (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"language":[{"iso":"eng"}],"volume":202,"date_created":"2023-10-29T23:01:17Z","scopus_import":"1","day":"30","author":[{"first_name":"Ilia","last_name":"Markov","id":"D0CF4148-C985-11E9-8066-0BDEE5697425","full_name":"Markov, Ilia"},{"full_name":"Vladu, Adrian","last_name":"Vladu","first_name":"Adrian"},{"first_name":"Qi","last_name":"Guo","full_name":"Guo, Qi"},{"orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}],"oa_version":"Preprint","title":"Quantized distributed training of large models with convergence guarantees","publication_status":"published","publication_identifier":{"eissn":["2640-3498"]},"acknowledged_ssus":[{"_id":"ScienComp"}],"intvolume":"       202","abstract":[{"lang":"eng","text":"Communication-reduction techniques are a popular way to improve scalability in data-parallel training of deep neural networks (DNNs). The recent emergence of large language models such as GPT has created the need for new approaches to exploit data-parallelism. Among these, fully-sharded data parallel (FSDP) training is highly popular, yet it still encounters scalability bottlenecks. One reason is that applying compression techniques to FSDP is challenging: as the vast majority of the communication involves the model’s weights, direct compression alters convergence and leads to accuracy loss. We present QSDP, a variant of FSDP which supports both gradient and weight quantization with theoretical guarantees, is simple to implement and has essentially no overheads. To derive QSDP we prove that a natural modification of SGD achieves convergence even when we only maintain quantized weights, and thus the domain over which we train consists of quantized points and is, therefore, highly non-convex. We validate this approach by training GPT-family models with up to 1.3 billion parameters on a multi-node cluster. Experiments show that QSDP preserves model accuracy, while completely removing the communication bottlenecks of FSDP, providing end-to-end speedups of up to 2.2x."}]},{"date_published":"2022-11-01T00:00:00Z","conference":{"end_date":"2022-11-11","start_date":"2022-11-07","name":"Middleware: International Middleware Conference","location":"Quebec, QC, Canada"},"acknowledgement":"The authors sincerely thank Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler and Bapi Chatterjee for useful discussions throughout the development of this project.","publication":"Proceedings of the 23rd ACM/IFIP International Middleware Conference","status":"public","external_id":{"arxiv":["2111.08617"]},"year":"2022","quality_controlled":"1","ddc":["000"],"page":"241-254","type":"conference","_id":"12780","date_updated":"2023-04-03T06:21:04Z","publisher":"Association for Computing Machinery","article_processing_charge":"Yes (via OA deal)","doi":"10.1145/3528535.3565248","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Markov I, Ramezanikebrya H, Alistarh D-A. CGX: Adaptive system support for communication-efficient deep learning. In: <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i>. Association for Computing Machinery; 2022:241-254. doi:<a href=\"https://doi.org/10.1145/3528535.3565248\">10.1145/3528535.3565248</a>","short":"I. Markov, H. Ramezanikebrya, D.-A. Alistarh, in:, Proceedings of the 23rd ACM/IFIP International Middleware Conference, Association for Computing Machinery, 2022, pp. 241–254.","ieee":"I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support for communication-efficient deep learning,” in <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i>, Quebec, QC, Canada, 2022, pp. 241–254.","chicago":"Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.” In <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i>, 241–54. Association for Computing Machinery, 2022. <a href=\"https://doi.org/10.1145/3528535.3565248\">https://doi.org/10.1145/3528535.3565248</a>.","ista":"Markov I, Ramezanikebrya H, Alistarh D-A. 2022. CGX: Adaptive system support for communication-efficient deep learning. Proceedings of the 23rd ACM/IFIP International Middleware Conference. Middleware: International Middleware Conference, 241–254.","mla":"Markov, Ilia, et al. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.” <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i>, Association for Computing Machinery, 2022, pp. 241–54, doi:<a href=\"https://doi.org/10.1145/3528535.3565248\">10.1145/3528535.3565248</a>.","apa":"Markov, I., Ramezanikebrya, H., &#38; Alistarh, D.-A. (2022). CGX: Adaptive system support for communication-efficient deep learning. In <i>Proceedings of the 23rd ACM/IFIP International Middleware Conference</i> (pp. 241–254). Quebec, QC, Canada: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3528535.3565248\">https://doi.org/10.1145/3528535.3565248</a>"},"language":[{"iso":"eng"}],"oa":1,"file":[{"file_id":"12795","date_created":"2023-04-03T06:17:58Z","file_size":1514169,"creator":"dernst","date_updated":"2023-04-03T06:17:58Z","relation":"main_file","checksum":"1a397746235f245da5468819247ff663","success":1,"file_name":"2022_ACMMiddleware_Markov.pdf","access_level":"open_access","content_type":"application/pdf"}],"department":[{"_id":"DaAl"}],"arxiv":1,"month":"11","file_date_updated":"2023-04-03T06:17:58Z","publication_identifier":{"isbn":["9781450393409"]},"publication_status":"published","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"text":"The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-to-point communication, and in particular via hardware bandwidth over-provisioning. Overprovisioning comes at a cost: there is an order of magnitude price difference between \"cloud-grade\" servers with such support, relative to their popular \"consumer-grade\" counterparts, although single server-grade and consumer-grade GPUs can have similar computational envelopes.\r\n\r\nIn this paper, we show that the costly hardware overprovisioning approach can be supplanted via algorithmic and system design, and propose a framework called CGX, which provides efficient software support for compressed communication in ML applications, for both multi-GPU single-node training, as well as larger-scale multi-node training. CGX is based on two technical advances: At the system level, it relies on a re-developed communication stack for ML frameworks, which provides flexible, highly-efficient support for compressed communication. At the application level, it provides seamless, parameter-free integration with popular frameworks, so that end-users do not have to modify training recipes, nor significant training code. This is complemented by a layer-wise adaptive compression technique which dynamically balances compression gains with accuracy preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy.","lang":"eng"}],"has_accepted_license":"1","date_created":"2023-03-31T06:17:00Z","title":"CGX: Adaptive system support for communication-efficient deep learning","oa_version":"Published Version","day":"01","author":[{"last_name":"Markov","full_name":"Markov, Ilia","id":"D0CF4148-C985-11E9-8066-0BDEE5697425","first_name":"Ilia"},{"full_name":"Ramezanikebrya, Hamidreza","last_name":"Ramezanikebrya","first_name":"Hamidreza"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian"}]},{"author":[{"first_name":"Karen","last_name":"Klein","id":"3E83A2F8-F248-11E8-B48F-1D18A9856A87","full_name":"Klein, Karen"},{"full_name":"Pascual Perez, Guillermo","id":"2D7ABD02-F248-11E8-B48F-1D18A9856A87","last_name":"Pascual Perez","orcid":"0000-0001-8630-415X","first_name":"Guillermo"},{"full_name":"Walter, Michael","id":"488F98B0-F248-11E8-B48F-1D18A9856A87","last_name":"Walter","first_name":"Michael","orcid":"0000-0003-3186-2482"},{"last_name":"Kamath Hosdurg","id":"4BD3F30E-F248-11E8-B48F-1D18A9856A87","full_name":"Kamath Hosdurg, Chethan","first_name":"Chethan"},{"full_name":"Capretto, Margarita","last_name":"Capretto","first_name":"Margarita"},{"last_name":"Cueto Noval","id":"ffc563a3-f6e0-11ea-865d-e3cce03d17cc","full_name":"Cueto Noval, Miguel","first_name":"Miguel"},{"last_name":"Markov","full_name":"Markov, Ilia","id":"D0CF4148-C985-11E9-8066-0BDEE5697425","first_name":"Ilia"},{"first_name":"Michelle X","full_name":"Yeo, Michelle X","id":"2D82B818-F248-11E8-B48F-1D18A9856A87","last_name":"Yeo"},{"last_name":"Alwen","id":"2A8DFA8C-F248-11E8-B48F-1D18A9856A87","full_name":"Alwen, Joel F","first_name":"Joel F"},{"id":"3E04A7AA-F248-11E8-B48F-1D18A9856A87","full_name":"Pietrzak, Krzysztof Z","last_name":"Pietrzak","first_name":"Krzysztof Z","orcid":"0000-0002-9139-1654"}],"day":"26","title":"Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement","oa_version":"Preprint","date_created":"2021-09-27T13:46:27Z","abstract":[{"lang":"eng","text":"While messaging systems with strong security guarantees are widely used in practice, designing a protocol that scales efficiently to large groups and enjoys similar security guarantees remains largely open. The two existing proposals to date are ART (Cohn-Gordon et al., CCS18) and TreeKEM (IETF, The Messaging Layer Security Protocol, draft). TreeKEM is the currently considered candidate by the IETF MLS working group, but dynamic group operations (i.e. adding and removing users) can cause efficiency issues. In this paper we formalize and analyze a variant of TreeKEM which we term Tainted TreeKEM (TTKEM for short). The basic idea underlying TTKEM was suggested by Millican (MLS mailing list, February 2018). This version is more efficient than TreeKEM for some natural distributions of group operations, we quantify this through simulations.Our second contribution is two security proofs for TTKEM which establish post compromise and forward secrecy even against adaptive attackers. The security loss (to the underlying PKE) in the Random Oracle Model is a polynomial factor, and a quasipolynomial one in the Standard Model. Our proofs can be adapted to TreeKEM as well. Before our work no security proof for any TreeKEM-like protocol establishing tight security against an adversary who can adaptively choose the sequence of operations was known. We also are the first to prove (or even formalize) active security where the server can arbitrarily deviate from the protocol specification. Proving fully active security – where also the users can arbitrarily deviate – remains open."}],"publication_status":"published","month":"08","department":[{"_id":"KrPi"},{"_id":"DaAl"}],"oa":1,"language":[{"iso":"eng"}],"citation":{"ama":"Klein K, Pascual Perez G, Walter M, et al. Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. In: <i>2021 IEEE Symposium on Security and Privacy </i>. IEEE; 2021:268-284. doi:<a href=\"https://doi.org/10.1109/sp40001.2021.00035\">10.1109/sp40001.2021.00035</a>","short":"K. Klein, G. Pascual Perez, M. Walter, C. Kamath Hosdurg, M. Capretto, M. Cueto Noval, I. Markov, M.X. Yeo, J.F. Alwen, K.Z. Pietrzak, in:, 2021 IEEE Symposium on Security and Privacy , IEEE, 2021, pp. 268–284.","ieee":"K. Klein <i>et al.</i>, “Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement,” in <i>2021 IEEE Symposium on Security and Privacy </i>, San Francisco, CA, United States, 2021, pp. 268–284.","chicago":"Klein, Karen, Guillermo Pascual Perez, Michael Walter, Chethan Kamath Hosdurg, Margarita Capretto, Miguel Cueto Noval, Ilia Markov, Michelle X Yeo, Joel F Alwen, and Krzysztof Z Pietrzak. “Keep the Dirt: Tainted TreeKEM, Adaptively and Actively Secure Continuous Group Key Agreement.” In <i>2021 IEEE Symposium on Security and Privacy </i>, 268–84. IEEE, 2021. <a href=\"https://doi.org/10.1109/sp40001.2021.00035\">https://doi.org/10.1109/sp40001.2021.00035</a>.","ista":"Klein K, Pascual Perez G, Walter M, Kamath Hosdurg C, Capretto M, Cueto Noval M, Markov I, Yeo MX, Alwen JF, Pietrzak KZ. 2021. Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. 2021 IEEE Symposium on Security and Privacy . SP: Symposium on Security and Privacy, 268–284.","mla":"Klein, Karen, et al. “Keep the Dirt: Tainted TreeKEM, Adaptively and Actively Secure Continuous Group Key Agreement.” <i>2021 IEEE Symposium on Security and Privacy </i>, IEEE, 2021, pp. 268–84, doi:<a href=\"https://doi.org/10.1109/sp40001.2021.00035\">10.1109/sp40001.2021.00035</a>.","apa":"Klein, K., Pascual Perez, G., Walter, M., Kamath Hosdurg, C., Capretto, M., Cueto Noval, M., … Pietrzak, K. Z. (2021). Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. In <i>2021 IEEE Symposium on Security and Privacy </i> (pp. 268–284). San Francisco, CA, United States: IEEE. <a href=\"https://doi.org/10.1109/sp40001.2021.00035\">https://doi.org/10.1109/sp40001.2021.00035</a>"},"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","doi":"10.1109/sp40001.2021.00035","article_processing_charge":"No","publisher":"IEEE","date_updated":"2023-09-07T13:32:11Z","_id":"10049","type":"conference","page":"268-284","main_file_link":[{"open_access":"1","url":"https://eprint.iacr.org/2019/1489"}],"quality_controlled":"1","year":"2021","related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"10035"}]},"project":[{"name":"International IST Doctoral Program","grant_number":"665385","call_identifier":"H2020","_id":"2564DBCA-B435-11E9-9278-68D0E5697425"},{"_id":"258AA5B2-B435-11E9-9278-68D0E5697425","grant_number":"682815","name":"Teaching Old Crypto New Tricks","call_identifier":"H2020"}],"status":"public","publication":"2021 IEEE Symposium on Security and Privacy ","ec_funded":1,"acknowledgement":"The first three authors contributed equally to this work. Funded by the European Research Council (ERC) under the European Union’s Horizon2020 research and innovation programme (682815-TOCNeT). Funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.665385.","conference":{"location":"San Francisco, CA, United States","name":"SP: Symposium on Security and Privacy","start_date":"2021-05-24","end_date":"2021-05-27"},"date_published":"2021-08-26T00:00:00Z"},{"department":[{"_id":"DaAl"}],"arxiv":1,"month":"05","issue":"10","citation":{"ama":"Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In: <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. Vol 35. ; 2021:9037-9045.","short":"G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 9037–9045.","ieee":"G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.","ista":"Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. 2021. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence vol. 35, 9037–9045.","chicago":"Nadiradze, Giorgi, Ilia Markov, Bapi Chatterjee, Vyacheslav  Kungurtsev, and Dan-Adrian Alistarh. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” In <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 35:9037–45, 2021.","mla":"Nadiradze, Giorgi, et al. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, vol. 35, no. 10, 2021, pp. 9037–45.","apa":"Nadiradze, G., Markov, I., Chatterjee, B., Kungurtsev, V., &#38; Alistarh, D.-A. (2021). Elastic consistency: A practical consistency model for distributed stochastic gradient descent. In <i>Proceedings of the AAAI Conference on Artificial Intelligence</i> (Vol. 35, pp. 9037–9045). Virtual."},"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","oa":1,"language":[{"iso":"eng"}],"volume":35,"date_created":"2021-12-09T09:21:35Z","author":[{"id":"3279A00C-F248-11E8-B48F-1D18A9856A87","full_name":"Nadiradze, Giorgi","last_name":"Nadiradze","first_name":"Giorgi","orcid":"0000-0001-5634-0731"},{"id":"D0CF4148-C985-11E9-8066-0BDEE5697425","full_name":"Markov, Ilia","last_name":"Markov","first_name":"Ilia"},{"first_name":"Bapi","orcid":"0000-0002-2742-4028","id":"3C41A08A-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Bapi","last_name":"Chatterjee"},{"first_name":"Vyacheslav ","last_name":"Kungurtsev","full_name":"Kungurtsev, Vyacheslav "},{"first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}],"day":"18","oa_version":"Published Version","title":"Elastic consistency: A practical consistency model for distributed stochastic gradient descent","publication_status":"published","abstract":[{"text":"One key element behind the recent progress of machine learning has been the ability to train machine learning models in large-scale distributed shared-memory and message-passing environments. Most of these models are trained employing variants of stochastic gradient descent (SGD) based optimization, but most methods involve some type of consistency relaxation relative to sequential SGD, to mitigate its large communication or synchronization costs at scale. In this paper, we introduce a general consistency condition covering communication-reduced and asynchronous distributed SGD implementations. Our framework, called elastic consistency, decouples the system-specific aspects of the implementation from the SGD convergence requirements, giving a general way to obtain convergence bounds for a wide variety of distributed SGD methods used in practice. Elastic consistency can be used to re-derive or improve several previous convergence bounds in message-passing and shared-memory settings, but also to analyze new models and distribution schemes. As a direct application, we propose and analyze a new synchronization-avoiding scheduling scheme for distributed SGD, and show that it can be used to efficiently train deep convolutional models for image classification.","lang":"eng"}],"intvolume":"        35","year":"2021","external_id":{"arxiv":["2001.05918"]},"related_material":{"record":[{"id":"10429","status":"public","relation":"dissertation_contains"}]},"ec_funded":1,"conference":{"location":"Virtual","name":"AAAI: Association for the Advancement of Artificial Intelligence","start_date":"2021-02-02","end_date":"2021-02-09"},"acknowledgement":"We would like to thank Christopher De Sa for his feedback on an earlier draft of this paper, as well as the anonymous AAAI reviewers for their useful comments. This project has received\r\nfunding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). Bapi\r\nChatterjee was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 754411 (ISTPlus).","date_published":"2021-05-18T00:00:00Z","project":[{"name":"ISTplus - Postdoctoral Fellowships","grant_number":"754411","call_identifier":"H2020","_id":"260C2330-B435-11E9-9278-68D0E5697425"},{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223"}],"publication":"Proceedings of the AAAI Conference on Artificial Intelligence","status":"public","date_updated":"2023-09-07T13:31:39Z","_id":"10432","type":"conference","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://ojs.aaai.org/index.php/AAAI/article/view/17092"}],"quality_controlled":"1","page":"9037-9045"}]