FedAR+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments

With the enhancement of people\u27s living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. As household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels that may appear when an appliance gets connected to a non-designated smart plug. While addressing these issues jointly, we propose a novel federated learning approach to appliance recognition, called FedAR+, enabling decentralized model training across clients in a privacy-preserving way even with mislabeled training data. FedAR+ introduces an adaptive noise handling method, essentially a joint loss function incorporating weights and label distribution, to empower the appliance recognition model against noisy labels. By deploying smart plugs in an apartment complex, we collect a labeled dataset that, along with two existing datasets, are utilized to evaluate the performance of FedAR+. Experimental results show that our approach can effectively handle up to 30% concentration of noisy labels while outperforming the prior solutions by a large margin on accuracy

Is Performance Fairness Achievable In Presence Of Attackers Under Federated Learning?

In the last few years, Federated Learning (FL) has received extensive attention from the research community because of its capability for privacy-preserving, collaborative learning from heterogeneous data sources. Most FL studies focus on either average performance improvement or the robustness to attacks, while some attempt to solve both jointly. However, the performance disparities across clients in the presence of attackers have largely been unexplored. In this work, we propose a novel Fair Federated Learning scheme with Attacker Detection capability (abbreviated as FFL+AD) to minimize performance discrepancies across benign participants. FFL+AD enables the server to identify attackers and learn their malign intent (e.g., targeted label) by investigating suspected models via top performers. This two-step detection method helps reduce false positives. Later, we introduce fairness by regularizing the benign clients\u27 local objectives with a variable boosting parameter that gives more emphasis on low performers in optimization. Under standard assumptions, FFL+AD exhibits a convergence rate similar to FedAvg. Experimental results show that our scheme builds a more fair and more robust model, under label-flipping and backdoor attackers, compared to prior schemes. FFL+AD achieves competitive accuracy even when 40% of the clients are attackers

Development of a mathematical model for simulation of coal crushing in a Hammer mill

Using the basic size-mass balance size reduction model for a continuous crushing operation and invoking the concept of a perfectly mixed system, a mathematical relationship has been obtained between the feed and product size distributions for a hammer mill. The model parameters ‘breakage distribution function’ for different size fractions of a Prime Coking Coal and an imported coal were determined by conducting appropriate tests in a laboratory hammer mill. Using data generated in the Rourkela Steel Plant on a Production hammer mill, variation of the second set of model parameters, ‘absolute rate of breakage of particles of each size class’, with coal feed rate to the mill was established. Analysis of the data generated in the plant has shown that only +10 mm particles of the imported coal broke faster than the PCC particles of the same size. The developed mathematical model can be used to stimulate the performance of the crushing plant in respect of the effect of feed size distribution and feed rate, and for taking control action for keeping the product fineness constant by adjusting the coal feed rate to the hammer mill

Stiffness Characteristics of Fibre-reinforced Composite Shaft Embedded with Shape Memory Alloy Wires

Frequent coast up/coast down operations of rotating shafts in the power and aerospace industry expose the flexible rotors to the risk of fatigue failures. Resonant vibrations during passage through critical speeds induce large stresses that may lead to failures. In this paper, the use of nitinol [shape memory alloy (SMA)] wires in the fibre-reinforced composite shaft, for the purpose ofmodifying shaft stiffness properties to avoid such failures, is discussed. A setup has been developed to fabricate the composite shaft (made of fibre glass and epoxy resin) embedded with pre-stressed SMA wires. Experiments have been carried out on the shaft to estimate the changes in the natural frequency of the composite shaft due to activation and deactivation ofSMA wires. The comparisonofthe experimental results with the established analytical results indicates feasibility ofvibration control using the special properties of SMA wires

Caesarean scar endometriosis: a rare site of extrapelvic endometriosis

Scar endometriosis is a rare presentation of extrapelvic endometriosis. A caesarean section scar is the most common site. The typical clinical presentation is that of a palpable firm subcutaneous nodule near surgical scars associated with cyclic pain and swelling during menses. It is often misdiagnosed with other abdominal wall and scar related pathological conditions. Diagnosis is mainly based upon a high index of suspicion. USG with color Doppler can clinch the diagnosis in patients with typical clinical features. FNAC may be inconclusive. MRI is the most sensitive but expensive modality to make the diagnosis. Wide local excision is the treatment of choice. We report a case of caesarean scar endometriosis and discuss about incidence, pathophysiology, diagnosis and treatment of this condition

Securing Federated Learning Against overwhelming Collusive Attackers

In the era of a data-driven society with the ubiquity of Internet of Things (IoT) devices storing large amounts of data localized at different places, distributed learning has gained a lot of traction, however, assuming independent and identically distributed data (iid) across the devices. While relaxing this assumption that anyway does not hold in reality due to the heterogeneous nature of devices, federated learning (FL) has emerged as a privacy-preserving solution to train a collaborative model over non-iid data distributed across a massive number of devices. However, the appearance of malicious devices (attackers), who intend to corrupt the FL model, is inevitable due to unrestricted participation. In this work, we aim to identify such attackers and mitigate their impact on the model, essentially under a setting of bidirectional label flipping attacks with collusion. We propose two graph theoretic algorithms, based on Minimum Spanning Tree and k-Densest graph, by leveraging correlations between local models. Our FL model can nullify the influence of attackers even when they are up to 70% of all the clients whereas prior works could not afford more than 50% of clients as attackers. The effectiveness of our algorithms is ascertained through experiments on two benchmark datasets, namely MNIST and Fashion-MNIST, with overwhelming attackers. We establish the superiority of our algorithms over the existing ones using accuracy, attack success rate, and early detection round

Preserving Privacy In Image Database Through Bit-planes Obfuscation

The recent surge in computer vision applications has caused visual privacy concerns to people who are either users or exposed to an underlying surveillance system. To preserve their privacy, image obfuscation lays out a strong road through which the usability of images can also be maintained without revealing any visual private information. However, prior solutions are susceptible to reconstruction attacks or produce non-trainable images even by leveraging the obfuscation ways. This paper proposes a novel bit-planes-based image obfuscation scheme, called Bimof, to protect the visual privacy of the user in the images that are input into a recognition-based system. By incorporating the chaotic system for non-invertible noise with matrix decomposition, Bimof offers strong security and usability for creating a secure image database. In Bimof, it is hard for an adversary to recover the original image, withstanding a malicious server. We conduct experiments on two standard activity recognition datasets, UCF101 and HMDB51, to validate the effectiveness and usability of our scheme. We provide a rigorous quantitative security analysis through pixel frequency attacks and differential analysis to support our findings

α-MSH inhibits induction of C/EBPβ-DNA binding activity and NOS2 gene transcription in macrophages

α-MSH inhibits induction of C/EBPβ-DNA binding activity and NOS2 gene transcription in macrophages.Backgroundα-Melanocyte–stimulating hormone (α-MSH) is an endogenous tridecapeptide that exerts anti-inflammatory actions and abrogates postischemic renal injury in rodents. α-MSH inhibits lipopolysaccharide (LPS)-induced gene expression of several cytokines, chemokines, and nitric oxide synthase-2 (NOS2), but the molecular mechanisms underlying these effects have not been clearly defined. To test the hypothesis that α-MSH inhibits the expression of inducible trans-activating factors involved in NOS2 regulation, we used RAW 264.7 macrophage cells to examine the effects of α-MSH on the activation of nuclear factor-кB (NF-кB) and CCAAT/enhancer binding protein-β (C/EBPβ), trans-acting factors known to be involved in LPS + interferon (IFN)-γ induction of the NOS2 gene.MethodsGel shift assays were performed to identify NF-кB and C/EBP DNA binding activities in LPS + IFN-γ–treated RAW 264.7 cells in the presence and absence of α-MSH. NOS2 promoter assays were conducted to identify the effects of α-MSH on LPS + IFN-γ–mediated induction of NOS2 transcription.ResultsGel shift assays demonstrated LPS + IFN-γ induction of NF-кB and C/EBP family protein-DNA complexes in nuclei harvested from the cells. Supershift assays revealed that the C/EBP complexes were comprised of C/EBPβ, but not C/EBPα, C/EBPα, or C/EBPϵ. α-MSH (100 nmol/L) inhibited the LPS + IFN-γ–mediated induction of nuclear DNA binding activity of C/EBPβ, but not that of NF-кB (in contrast to reports in other cell types), as well as the activity of a murine NOS2 promoter-luciferase construct. In contrast, α-MSH (100 nmol/L) had no effect on the induction of NOS2 promoter-luciferase genes harboring deletion or mutation of the C/EBP box.ConclusionsThese data indicate that α-MSH inhibits the induction of C/EBPβ DNA binding activity and that this effect is a major mechanism by which α-MSH inhibits the transcription of the NOS2 gene. The inability of α-MSH to inhibit LPS + IFN-γ induction of NF-кB in murine macrophage cells, which contrasts with inhibitory effects of the neuropeptide in other cell types, suggests that cell-type–specific mechanisms are involved

ClimaX: A foundation model for weather and climate

Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models are computationally intensive, especially when modeling the atmospheric phenomenon at a fine-grained spatial and temporal resolution. Recent data-driven approaches based on machine learning instead aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the generality of numerical models. We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings. ClimaX extends the Transformer architecture with novel encoding and aggregation blocks that allow effective use of available compute while maintaining general utility. ClimaX is pre-trained with a self-supervised learning objective on climate datasets derived from CMIP6. The pre-trained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatio-temporal scales unseen during pretraining. Compared to existing data-driven baselines, we show that this generality in ClimaX results in superior performance on benchmarks for weather forecasting and climate projections, even when pretrained at lower resolutions and compute budgets. The source code is available at https://github.com/microsoft/ClimaX.Comment: International Conference on Machine Learning 202