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Deep Equilibrium Models Meet Federated Learning

Deep Equilibrium Models Meet Federated Learning

引用

European Signal Processing Conference (EUSIPCO)

作者： Alexandros Gkillas Dimitris Ampeliotis Kostas Berberidis Dept. of Computer Engineering and Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the sharing large models and the ability to incorporate heterogeneous edge devices with significantly different computation capabilities. Additionally, a weighted average fusion rule is proposed at the server-side of the FL framework to account for the different qualities of models from heterogeneous edge devices. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning, contributing to the development of an efficient and effective FL framework. Finally, promising initial experimental results are presented, demonstrating the potential of this approach in addressing challenges of FL.

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Time-Stretched Imaging Flow Cytometry and Photonic Neuromorphic Processing for Particle Classification

Time-Stretched Imaging Flow Cytometry and Photonic Neuromorp...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Tsirigotis, Aris Deligiannidis, Stavros Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. 83200 Karlovasi Samos Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece

ISBN: (纸本)9798350345995

In this work, we investigate the compression capabilities of a photonic neuromorphic accelerator relying on an optical spectrum slicing technique [1], following a high-flow 1D imaging cytometry setup, able to image 62000 particles/sec. The utilized image scheme relies on a technique, where spatial information is transfered to the spectrum and subsequently through dispersion to the temporal domain. Therefore, it can replace high speed spectrometers with a single photodetector [2]. On the other hand, the elevated image acquisition rate, trigger the generation of a staggering amount of data, which can cause a processing backlog at the digital backend. In this context, we explore, for the first time, the use of a photonic neuromorphic pre-processing unit, following the cytometer that can reduce the number of trainable parameters at a lightweight digital backend by a factor of 2.5, while at the same time, it can project incoming data to a high dimensional space boosting accuracy. © 2023 IEEE.

关键词： Photonics

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ML-Inspired Multiple Relay Selection in UAV-Enabled Untrusted Networks

ML-Inspired Multiple Relay Selection in UAV-Enabled Untruste...

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IEEE Conference on Wireless Communications and Networking

作者： Lefteris Tsipi Emmanouel T. Michailidis Konstantinos Maliatsos Demosthenes Vouyioukas Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece Dept. of Informatics and Computer Engineering University of West Attica Egaleo Greece Dept. of Digital Systems University of Piraeus Piraeus Greece

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, multiple relay selection (MRS) schemes for untrusted unmanned aerial vehicle (UAV)-enabled networks are proposed. In this context, various machine learning (ML) models are employed to improve secrecy performance by optimally selecting multiple aerial relays from the available ones, all of which are regarded as untrusted. Notably, these ML models can cope with the quickly changing and random positioning of the aerial relays, effectively decoupling the intricate coupling relationship between the secrecy rate, channel coefficients, and inter-node distances. The results indicate that the ML-based MRS schemes obtain sufficient accuracy and better decoupling than a respective exhaustive searching (ES) approach, while also maintaining a lower computational complexity.

关键词： Couplings Accuracy Predictive models Autonomous aerial vehicles Security Ensemble learning Relays Computational complexity Context modeling

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Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications

Resource Efficient Federated Learning for Deep Anomaly Detec...

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International Conference on Digital Signal Processing (DSP)

作者： Alexandros Gkillas Aris Lalos Athena Research Center Industrial Systems Institute Greece Dept. of Computer Engineering & Informatics University of Patras

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%.

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Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

Neuromorphic Camera Assisted High-Flow Imaging Cytometry for...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Tsilikas, Ioannis Deligiannidis, Stavros Tsirigotis, Aris Tsigaridas, Georgios N. Bogris, Adonis Mesaritakis, Charis National Technical University of Athens Dept. Physics Iroon Polytechniou 9 Zografou Athens15772 Greece University of West Attica Dept. Informatics and Computer Engineering Ag. Spiridonos Egaleo12243 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Karlovasi Samos83200 Greece

ISBN: (纸本)9798350345995

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach. © 2023 IEEE.

关键词： Cameras

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A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization 7

A Study on Text Feature Selection Using Ant Colony and Grey ...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Widians, Joan Angelina Wardoyo, Retantyo Hartati, Sri Universitas Gadjah Mada Doctoral Program Dept. of Computer Science and Electronics Indonesia Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Faculty of Mathematics and Natural Sciences Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms. © 2022 IEEE.

关键词： Ant colony optimization

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Approximating Edit Distance in the Fully Dynamic Model

Approximating Edit Distance in the Fully Dynamic Model

引用

Annual IEEE Symposium on Foundations of computer Science

作者： Tomasz Kociumaka Anish Mukherjee Barna Saha Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Dept. of Computer Science University of Warwick Coventry UK Dept. of Computer Science & Engineering University of California San Diego US

The edit distance is a fundamental measure of sequence similarity, defined as the minimum number of character insertions, deletions, and substitutions needed to transform one string into the other. Given two strings of length at most n, a simple dynamic programming computes their edit distance exactly in $\mathcal{O}\left(n^{2}\right)$ time, which is also the best possible (up to subpolynomial factors) assuming the Strong Exponential Time Hypothesis (SETH). The last few decades have seen tremendous progress in edit distance approximation, where the runtime has been brought down to subquadratic, to near-linear, and even to sublinear at the cost of approximation. In this paper, we study the dynamic edit distance problem where the strings change dynamically as the characters are substituted, inserted, or deleted over time. Each change may happen at any location of either of the two strings. The goal is to maintain the (exact or approximate) edit distance of such dynamic strings while minimizing the update time. The exact edit distance can be maintained in $\mathcal{O}\left(n \log ^{2} n\right)$ time per update (Charalampopoulos, Kociumaka, Mozes; 2020), which is again tight assuming SETH. Unfortunately, even with the unprecedented progress in edit distance approximation in the static setting, strikingly little is known regarding dynamic edit distance approximation. Utilizing the best near-linear-time (Andoni, Nosatzki; 2020) and sublinear-time (Goldenberg, Kociumaka, Krauthgamer, Saha; 2022) approximation algorithm, an old exact algorithm (Landau and Vishkin; 1988), and a generic dynamic strings implementation (Mehlhorn, Sundar, Uhrig; 1996), it is possible to achieve an $\mathcal{O}\left(n^{c}\right)$-approximation in $n^{0.5-c+o(1)}$ update time for any constant $c \in\left[0, \frac{1}{6}\right]$. Improving upon this trade-off, characterized by the approximation-ratio and update-time product $n^{0.5+o(1)}$, remains wide open. The contribution of this work is a dynami

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A bi-directional shortest path calculation speed up technique for RDBMS 7

A bi-directional shortest path calculation speed up techniqu...

引用

7th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Koritsoglou, Kyriakos Laskas, Petros Patras, Vaios Boursianis, Achilles D. Grigoriadis, Konstantinos Fudos, Ioannis University of Ioannina Dept. of Informatics and Telecommunications Arta Greece University of Ioannina Dept of Computer Science and Engineering Ioannina Greece Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In Relational Database Management Systems (RDBMS) the representation and storage of graph datasets is achieved by using two data tables. One representing the nodes' data and another for the edges' data of the graph. Using this model every search process in a big data volume requires multiple joins of large data tables, which significantly reduces their performance. The increasing use of graph datasets in various computer science fields such as (big data, social and computer networks, networks, Semantic Web, knowledge graphs, biological networks, and road networks) creates the need to research methods that will improve the performance of RDBMS when managing this type of data. In this paper, unlike most of the solutions proposed in the literature to date, a purely relational approach is presented using only the native RDBMS query analyzer and not either aggregate functions not implemented in all RDBMSs or an additional plugin layer that analyzes and processes the graph queries in order to translate them into the corresponding SQL versions. With the proposed solution we proved that by designing a bi-directional BFS algorithm that passes through properly selected intermediate pivot points, it is possible to query graph datasets significantly larger than those a complex SQL query can handle, with no major overhead. © 2022 IEEE.

关键词： Digital storage

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A Highly Interpretable Deep Equilibrium Network for Hyperspectral Image Deconvolution

A Highly Interpretable Deep Equilibrium Network for Hyperspe...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Alexandros Gkillas Dimitris Ampeliotis Kostas Berberidis Dept. of Computer Engineering & Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

In this paper, a novel technique for the hyperspectral image deconvolution problem is developed. First, considering the highly ill-posed nature of the examined problem, it is imperative to incorporate proper priors (regularizers) to capture the strong spectral and spatial dependencies of the hyperspectral images. Then, in light of this, a novel optimization problem is proposed by employing a convolutional neural network to act as a regularizer, which is learnt to reflect the properties of the signals of interest. To solve the proposed optimization problem, we use the half quadratic splitting methodology, thus designing an efficient iterative solver (iteration map). Based on the Deep Equilibrium (DEQ) modeling, which aims to express the proposed iterative solver as an equilibrium (fixed-point) computation, a highly interpretable deep learning-based network is derived, which can be trained endto-end. Extensive numerical results using two publicly available datasets illustrate that the proposed method markedly outperforms other state-of-the-art approaches.

关键词： Deep learning Deconvolution Signal processing algorithms Signal processing Numerical models Iterative methods Convolutional neural networks

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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