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Federated Learning in Named Entity Recognition
Ch. 8. P. 90–101.
Efim Luboshnikov, Makarov I.
This article is devoted to the implementation of the federated approach to named entity recognition. The novel federated approach is designed to solve data privacy issues. The classic BiLSTM-CNNs-CRF and its modifications trained on a single machine are taken as baseline. Federated training is conducted for them. Influence of use of pretrained embedding, use of various blocks of architecture on training and quality of final model is considered. Besides, other important questions arising in practice are considered and solved, for example, creation of distributed private dictionaries, selection of base model for federated learning.
Ali J. Dayoub, Ehab S. Suleiman, , in: Proceedings of the 2026 8th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE).: IEEE, 2026. Ch. 159 P. 1–5.
Abstract— Urban traffic congestion costs the global economy over $1 trillion annually, necessitating intelligent traffic signal control (ITSC) solutions. Traditional centralized approaches face critical limitations: privacy violations from vehicle trajectory data sharing, prohibitive communication overhead, and scalability challenges in heterogeneous urban environments. This paper presents a federated reinforcement learning (FRL) framework for privacy-preserving traffic signal ...
Added: April 30, 2026
Demidovich Y., Petr Ostroukhov, Malinovsky G. et al., , in: The Thirteenth International Conference on Learning Representations: ICLR 2025.: ICLR, 2025.
Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic generalized $(L_0,L_1)$-smoothness assumption, though it remains largely unexplored. Many existing algorithms designed for standard smooth problems need to be revised. However, in the context of Federated Learning, only a few ...
Added: July 15, 2025
Paul M., Durmus A., Dieuleveut A. et al., , in: Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 3-5 May 2025, Splash Beach Resort in Mai Khao, Thailand, PMLR: vol. 258Vol. 258.: PMLR, 2025. Ch. 258 P. 5023–5031.
In this paper, we present a novel analysis of FedAvg with constant step size, relying on the Markov property of the underlying process. We demonstrate that the global iterates of the algorithm converge to a stationary distribution and analyze its resulting bias and variance relative to the problem’s solution. We provide a first-order bias expansion in ...
Added: May 18, 2025
Plassier V., Kotelevskii N., Rubashevskii A. et al., , in: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), 2-4 May 2024, Palau de Congressos, Valencia, Spain. PMLR: Volume 238Vol. 238.: Valencia: PMLR, 2024. P. 4879–4887.
Conformal prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods heavily rely on the data exchangeability, a condition often violated in practice. Existing approaches for tackling non-exchangeability lead to methods that are not computable beyond the simplest examples. In this ...
Added: May 30, 2024
Leconte L., Jonckheere M., Samsonov S. et al., , in: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), 2-4 May 2024, Palau de Congressos, Valencia, Spain. PMLR: Volume 238Vol. 238.: Valencia: PMLR, 2024. P. 1711–1719.
We study asynchronous federated learning mechanisms with nodes having potentially different computational speeds. In such an environment, each node is allowed to work on models with potential delays and contribute to updates to the central server at its own pace. Existing analyses of such algorithms typically depend on intractable quantities such as the maximum node ...
Added: May 26, 2024
Kolmogorova A., Зарембо В. С., Ткачева Е. С. et al., В кн.: Лингвистическая семантика в пространственном измерении: Словарь. Дискурс. Корпус.: Екатеринбург: Кабинетный ученый, 2024. Гл. 10 С. 423–445.
The purpose of this study is to describe the characteristics of the text of a popular Soviet song as a linguo-ideological phenomenon. The corpus of Soviet songs collected by the research group is used as material. The focus of this publication is on two characteristics: changes in the emotional tonality of popular songs released on ...
Added: December 10, 2023
Ali A., , in: 22nd International Conference, NEW2AN 2022, Tashkent, Uzbekistan, December 15–16, 2022, Proceedings. Internet of Things, Smart Spaces, and Next Generation Networks and Systems. LNCS, volume 13772Issue 13772.: Springer, 2023. P. 525–533.
Machine learning over distributed data collected by many clients has
important applications in use cases where data privacy is a key concern or central data storage is not an option. Federated learning has introduced solutions
for these scenarios, unlike the client-server approach, where all the training data
is centralized in the server side, the clients, in a federated ...
Added: May 18, 2023
Ali A., , in: 2022 International Conference on Smart Applications, Communications and Networking (SmartNets).: IEEE, 2022. P. 1–4.
Added: May 16, 2023
Loukachevitch N., Manandhar S., Baral E. et al., Bioinformatics 2023 Vol. 39 No. 4 Article btad161
Motivation
This paper describes NEREL-BIO – an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL (Loukachevitch et al., 2021) by introducing domain-specific entity types. NEREL-BIO annotation scheme covers both general and biomedical domains making it suitable for domain transfer experiments. NEREL-BIO ...
Added: April 5, 2023
Ali A., Koucheryavy E., Ebraheem A., В кн.: Инновационные, информационные и коммуникационные технологии. Сборник трудов XIX Международной научно-практической конференции.: М.: Ассоциация выпускников и сотрудников ВВИА им. проф. Жуковского, 2022. С. 38–42.
Added: February 22, 2023
Sadiev A., Borodich E., Beznosikov A. et al., EURO Journal on Computational Optimization 2022 Vol. 10 Article 100041
This paper considers the problem of decentralized, personalized federated learning. For centralized personalized federated learning, a penalty that measures the deviation from the local model and its average, is often added to the objective function. However, in a decentralized setting this penalty is expensive in terms of communication costs, so here, a different penalty — ...
Added: October 28, 2022
Soshnikov D. V., Soshnikova V., / Series Computer Science "arxiv.org". 2021.
Since the beginning of COVID pandemic, there have been around 700000 scientific papers published on the subject. A human researcher cannot possibly get acquainted with such a huge text corpus -- and therefore developing AI-based tools to help navigating this corpus and deriving some useful insights from it is highly needed. In this paper, we ...
Added: February 22, 2022
Loukachevitch N., Artemova E., Batura T. et al., , in: International Conference Recent Advances in Natural Language Processing, RANLP 2021.: Association for Computational Linguistics, 2021. P. 876–886.
In this paper, we present NEREL, a Rus- sian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations. Its important dif- ference from previous datasets is annotation of nested named entities, as well as relations within ...
Added: September 27, 2021
Davletov A., Gordeev D., Rei A. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: по материалам ежегодной международной конференции «Диалог» (Москва, 17–20 июня 2020 г.)Issue 19(26): дополнительный том.: -, 2020. P. 187–197.
In this work we present our system for RuREBus shared task held together with Dialog 2020 conference. The task consisted of 3 subtasks: named entity recognition, relation extraction with provided named entity tags and end-to-end relation extraction. Our system took the first and the second place in the first and the second subtasks respectively. For ...
Added: October 10, 2020
Ivanin V., Artemova E., Batura T. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: по материалам ежегодной международной конференции «Диалог» (Москва, 17–20 июня 2020 г.)Issue 19(26): дополнительный том.: -, 2020. P. 401–416.
In this paper, we present a shared task on core information extraction prob- lems, named entity recognition and relation extraction. In contrast to popular shared tasks on related problems, we try to move away from strictly aca- demic rigor and rather model a business case. As a source for textual data we choose the corpus ...
Added: June 21, 2020
Artemova E., Batura T., Sarkisyan V. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва, 17 июня — 20 июня 2020 г.)Вып. 19(26).: М.: Изд-во РГГУ, 2020. P. 416–432.
В статье представлены результаты соревнования по распознаванию именованных сущностей и извлечению отношений. Целью соревнования является сравнение методов извлечения сущностей и отношений на русском языке в постановке, приближенной к индустриальным задачам. В качестве исходной коллекции текстов использовался корпус Минэкономразвития РФ, содержащий программы стратегического развития. Корпус был размечен в соответствии с инструкцией, разработанной авторами статьи. В процессе ...
Added: June 11, 2020
Козеренко Е. Б., Кузнецов К. И., Romanov D. A., Информатика и ее применения 2018 Т. 12 № 3 С. 91–98
The paper presents the method for creation of knowledge extraction systems based on the approach employing the software tool system PullEnti comprising the algorithms for morphological and semantic-syntactical analysis which makes it possible to extract entities of certain types from natural language texts (persons, organizations, locations, and other target semantic objects). The PullEnti system uses ...
Added: December 19, 2018
Starostin A. S., Bocharov V. V., Alexeeva S. V. et al., , in: Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной международной конференции «Диалог» (Москва,1–4 июля 2016 г.)Вып. 15.: М.: Изд-во РГГУ, 2016. P. 688–705.
In this paper, we describe the rules and results of the FactRuEval informa- tion extraction competition held in 2016 as part of the Dialogue Evaluation initiative in the run-up to Dialogue 2016. The systems were to extract in- formation from Russian texts and competed in two named entity extraction tracks and one fact extraction track. ...
Added: October 7, 2016