Towards Benchmarking Power-Performance Characteristics of Federated Learning Clients

Federated Learning (FL) is a decentralized machine learning approach where local models are trained on distributed clients, allowing privacy-preserving collaboration by sharing model updates instead of raw data. However, the added communication overhead and increased training time caused by heterogenous data distributions results in higher energy consumption and carbon emissions for achieving similar model performance than traditional machine learning. At the same time, efficient usage of available energy is an important requirement for battery constrained devices. Because of this, many different approaches on energy-efficient and carbon-efficient FL scheduling and client selection have been published in recent years. However, most of this research oversimplifies power performance characteristics of clients by assuming that they always require the same amount of energy per processed sample throughout training. This overlooks real-world effects arising from operating devices under different power modes or the side effects of running other workloads in parallel. In this work, we take a first look on the impact of such factors and discuss how better power-performance estimates can improve energy-efficient and carbon-efficient FL scheduling.Comment: Machine Learning and Networking Workshop, NetSys 202

Offloading Real-Time Tasks in IIoT Environments under Consideration of Networking Uncertainties

Offloading is a popular way to overcome the resource and power constraints of networked embedded devices, which are increasingly found in industrial environments. It involves moving resource-intensive computational tasks to a more powerful device on the network, often in close proximity to enable wireless communication. However, many Industrial Internet of Things (IIoT) applications have real-time constraints. Offloading such tasks over a wireless network with latency uncertainties poses new challenges. In this paper, we aim to better understand these challenges by proposing a system architecture and scheduler for real-time task offloading in wireless IIoT environments. Based on a prototype, we then evaluate different system configurations and discuss their trade-offs and implications. Our design showed to prevent deadline misses under high load and network uncertainties and was able to outperform a reference scheduler in terms of successful task throughput. Under heavy task load, where the reference scheduler had a success rate of 5%, our design achieved a success rate of 60%.Comment: 2nd International Workshop on Middleware for the Edge (MiddleWEdge '23). 2023. AC

Quantum Rate-Distortion Coding of Relevant Information

IEEE Rate-distortion theory provides bounds for compressing data produced by an information source to a specified encoding rate that is strictly less than the sourceand#x2019;s entropy. This necessarily entails some loss, or distortion, between the original source data and the best approximation after decompression. The so-called Information Bottleneck Method is designed to compress only and#x2018;relevantand#x2019; information. Which information is relevant is determined by the correlation between the data being compressed and a variable of interest, so-called side information. In this paper, an Information Bottleneck Method is introduced for the compression of quantum data. The channel communication picture is used for compression and decompression. The rate of compression is derived using an entanglement assisted protocol with classical communication, and under an unproved conjecture that the rate function is convex in the distortion parameter. The optimum channel achieving this rate for a given input state is characterised. The conceptual difficulties arising due to differences in the mathematical formalism between quantum and classical probability theory are discussed and solutions are presented

PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning

Due to the complexity of modern IT services, failures can be manifold, occur at any stage, and are hard to detect. For this reason, anomaly detection applied to monitoring data such as logs allows gaining relevant insights to improve IT services steadily and eradicate failures. However, existing anomaly detection methods that provide high accuracy often rely on labeled training data, which are time-consuming to obtain in practice. Therefore, we propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows provided by monitoring systems instead of labeled data. Our attention-based model uses a novel objective function for weak supervision deep learning that accounts for imbalanced data and applies an iterative learning strategy for positive and unknown samples (PU learning) to identify anomalous logs. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets and detects anomalous log messages with an F1-score of more than 0.99 even within imprecise failure time windows

Carbon-Awareness in CI/CD

While the environmental impact of digitalization is becoming more and more evident, the climate crisis has become a major issue for society. For instance, data centers alone account for 2.7% of Europe's energy consumption today. A considerable part of this load is accounted for by cloud-based services for automated software development, such as continuous integration and delivery (CI/CD) workflows. In this paper, we discuss opportunities and challenges for greening CI/CD services by better aligning their execution with the availability of low-carbon energy. We propose a system architecture for carbon-aware CI/CD services, which uses historical runtime information and, optionally, user-provided information. We examined the potential effectiveness of different scheduling strategies using real carbon intensity data and 7,392 workflow executions of Github Actions, a popular CI/CD service. Our results show, that user-provided information on workflow deadlines can effectively improve carbon-aware scheduling.Comment: 21st International Conference on Service-Oriented Computing (ICSOC '24) Workshop

FedZero: Leveraging Renewable Excess Energy in Federated Learning

Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably introduces inefficiencies compared to centralized model training, which will further increase the already high energy usage and associated carbon emissions of machine learning in the future. Although the scheduling of workloads based on the availability of low-carbon energy has received considerable attention in recent years, it has not yet been investigated in the context of FL. However, FL is a highly promising use case for carbon-aware computing, as training jobs constitute of energy-intensive batch processes scheduled in geo-distributed environments. We propose FedZero, a FL system that operates exclusively on renewable excess energy and spare capacity of compute infrastructure to effectively reduce the training's operational carbon emissions to zero. Based on energy and load forecasts, FedZero leverages the spatio-temporal availability of excess energy by cherry-picking clients for fast convergence and fair participation. Our evaluation, based on real solar and load traces, shows that FedZero converges considerably faster under the mentioned constraints than state-of-the-art approaches, is highly scalable, and is robust against forecasting errors

Spleen Tyrosine Kinase Regulates AP-1 Dependent Transcriptional Response to Minimally Oxidized LDL

Oxidative modification of low-density lipoprotein (LDL) turns it into an endogenous ligand recognized by pattern-recognition receptors. We have demonstrated that minimally oxidized LDL (mmLDL) binds to CD14 and mediates TLR4/MD-2-dependent responses in macrophages, many of which are MyD88-independent. We have also demonstrated that the mmLDL activation leads to recruitment of spleen tyrosine kinase (Syk) to TLR4 and TLR4 and Syk phosphorylation. In this study, we produced a macrophage-specific Syk knockout mouse and used primary Syk−/− macrophages in our studies. We demonstrated that Syk mediated phosphorylation of ERK1/2 and JNK, which in turn phosphorylated c-Fos and c-Jun, respectively, as assessed by an in vitro kinase assay. c-Jun phosphorylation was also mediated by IKKε. c-Jun and c-Fos bound to consensus DNA sites and thereby completed an AP-1 transcriptional complex and induced expression of CXCL2 and IL-6. These results suggest that Syk plays a key role in TLR4-mediated macrophage responses to host-generated ligands, like mmLDL, with subsequent activation of an AP-1 transcription program

Das Elektronengasmodell in der Sekundarstufe I

Trotz mehrjähriger unterrichtlicher Bemühungen besitzen viele Schüler am Ende der Sekundarstufe I kein ausreichend entwickeltes Verständnis der elementaren Elektrizitätslehre. Insbesondere die Entwicklung eines unabhängigen Spannungsbegriffs stellt Schüler in der Sekundarstufe I vor enorme Schwierigkeiten. Ein Grund hierfür könnte neben der zentralen Stellung des Stromkonzepts auch in der didaktisch unbegründeten Fokussierung auf die Differenzgröße Spannung im traditionellen Unterricht liegen. Im Gegensatz zum Potenzial, das einem Leiterabschnitt lokal zugeordnet werden kann, bezieht sich die elektrische Spannung nämlich immer auf die Differenz zweier Potenzialwerte in einem Stromkreis. Es ist daher naheliegend anzunehmen, dass eine bildhaft-anschauungsorientierte Vorstellung des Potenzials entscheidend zu einem besseren Verständnis elektrischer Stromkreise beitragen kann.Das Elektronengasmodell stellt über die Gleichsetzung des Elektronengasdrucks mit dem elektrischen Potenzial einen vielversprechenden Ansatz dar, eine solche anschauliche Erklärung für das Potenzial zu liefern. Fachlich basiert das Elektronengasmodell auf den physikalischen Eigenschaften der wenig beachteten Oberflächenelektronen in elektrischen Stromkreisen, die im Gegensatz zu Leitungselektronen vor und nach einem Widerstand unterschiedliche Dichten aufweisen. Nach einer Einordnung in bisherige Unterrichtsansätze wird im Artikel daher der fachliche Hintergrund des Elektronengasmodells näher beleuchtet, bevor die für die Schule nötigen didaktischen Elementarisierungen sowie mögliche Stärken und Schwächen des Modells diskutiert werden.

Das Elektronengasmodell in der Sekundarstufe I

Trotz mehrjähriger unterrichtlicher Bemühungen besitzen viele Schüler am Ende der Sekundarstufe I kein ausreichend entwickeltes Verständnis der elementaren Elektrizitätslehre. Insbesondere die Entwicklung eines unabhängigen Spannungsbegriffs stellt Schüler in der Sekundarstufe I vor enorme Schwierigkeiten. Ein Grund hierfür könnte neben der zentralen Stellung des Stromkonzepts auch in der didaktisch unbegründeten Fokussierung auf die Differenzgröße Spannung im traditionellen Unterricht liegen. Im Gegensatz zum Potenzial, das einem Leiterabschnitt lokal zugeordnet werden kann, bezieht sich die elektrische Spannung nämlich immer auf die Differenz zweier Potenzialwerte in einem Stromkreis. Es ist daher naheliegend anzunehmen, dass eine bildhaft-anschauungsorientierte Vorstellung des Potenzials entscheidend zu einem besseren Verständnis elektrischer Stromkreise beitragen kann.Das Elektronengasmodell stellt über die Gleichsetzung des Elektronengasdrucks mit dem elektrischen Potenzial einen vielversprechenden Ansatz dar, eine solche anschauliche Erklärung für das Potenzial zu liefern. Fachlich basiert das Elektronengasmodell auf den physikalischen Eigenschaften der wenig beachteten Oberflächenelektronen in elektrischen Stromkreisen, die im Gegensatz zu Leitungselektronen vor und nach einem Widerstand unterschiedliche Dichten aufweisen. Nach einer Einordnung in bisherige Unterrichtsansätze wird im Artikel daher der fachliche Hintergrund des Elektronengasmodells näher beleuchtet, bevor die für die Schule nötigen didaktischen Elementarisierungen sowie mögliche Stärken und Schwächen des Modells diskutiert werden.

Towards a Staging Environment for the Internet of Things

Internet of Things (IoT) applications promise to make many aspects of our lives more efficient and adaptive through the use of distributed sensing and computing nodes. A central aspect of such applications is their complex communication behavior that is heavily influenced by the physical environment of the system. To continuously improve IoT applications, a staging environment is needed that can provide operating conditions representative of deployments in the actual production environments -- similar to what is common practice in cloud application development today. Towards such a staging environment, we present Marvis, a framework that orchestrates hybrid testbeds, co-simulated domain environments, and a central network simulation for testing distributed IoT applications. Our preliminary results include an open source prototype and a demonstration of a Vehicle-to-everything (V2X) communication scenario