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АДАПТАЦИЯ СТРАТЕГИЯ ДИФФУЗИИ ПО БЕСПРОВОДНЫМ КАНАЛАМ С ЗАМИРАНИЕМ
С. 38–42.
In book
М.: Ассоциация выпускников и сотрудников ВВИА им. проф. Жуковского, 2022.
Beznosikov A., Kormakov G., Grigorievskiy A. et al., Journal of Optimization Theory and Applications 2026 Vol. 209 Article 18
The objective of Vertical Federated Learning (VFL) is to collectively train a model using features available on different devices while sharing the same users. This paper focuses on the saddle point reformulation of the VFL problem via the classical Lagrangian function. We first demonstrate how this formulation can be solved using deterministic methods.More importantly, we explore various stochastic modifications to ...
Added: June 17, 2026
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
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
Koshcheev D., Isopeskul O., М.: ИНФРА-М, 2024.
Монография представляет собой одно из первых исследований концептуально-теоретического анализа как теоретико-методической области, составляющей основу обзорных научных работ. Впервые проведена систематизация методики и практики концептуально-теоретического анализа за период 1900-2022 гг., выделены основные подходы, представлены этапы их развития, а также описаны сильные и слабые стороны. Разработан авторский системно-критериальный подход к концептуально-теоретическому анализу, нивелировавший основные недостатки подходов-предшественников. Приведена ...
Added: February 18, 2024
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
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
Efim Luboshnikov, Makarov I., , in: Recent Trends in Analysis of Images, Social Networks and Texts. 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020 Revised Supplementary ProceedingsVol. 12602.: Springer, 2021. Ch. 8 P. 90–101.
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 ...
Added: March 24, 2021