Bound on the graviton mass from Chandra X-ray cluster sample

We present new limits on the graviton mass using a sample of 12 relaxed galaxy clusters, for which temperature and gas density profiles were derived by Vikhlinin et al (astro-ph/0507092) using Chandra X-ray observations. These limits can be converted to a bound on the graviton mass, assuming a non-zero graviton mass would lead to a Yukawa potential at these scales. For this purpose, we first calculate the total dynamical mass from the hydrostatic equilibrium equation in Yukawa gravity and then compare it with the corresponding mass in Newtonian gravity. We calculate a 90 % c.l. lower/upper limit on the graviton Compton wavelength/ mass for each of the 12 clusters in the sample. The best limit is obtained for Abell 2390, corresponding to $\lambda_g > 3.58\times 10^{19}$ km or $m_g<3.46 \times 10^{-29}$ eV. This is the first proof of principles demonstration of setting a limit on the graviton mass using a sample of related galaxy clusters with X-ray measurements and can be easily applied to upcoming X-ray surveys such as eRosita.Comment: 6 pages, 1 figur

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

Revitalization of thiazolidinedione the optimum agents to be combined with SGLT 2 inhibitors to optimize glycemic control and reduce cardiovascular mortality: randomized control trial

Background: Type 2 diabetes mellitus (T2DM) significantly increases morbidity and mortality from cardiovascular disease. The present study was conducted to know the effect of thiazolidinedione and SGLT2 inhibitor on glycemic control, blood pressure and lipid profile and effect on cardiovascular mortality in T2DM. Methods: A total 80 patients of aged ≥40 years with T2DM were included and divided into 4 groups based on ongoing treatment i.e., (lifestyle modification + Tab metformin 500mg BD) + 1) Tab metformin 500mg; 2) Tab dapagliflozin 10mg OD; 3) Tab pioglitazone 15mg OD; 4) Tab pioglitazone 15mg OD + Tab Dapagliflozin 10mg OD. Results: The change in FBS, PLBS and HbA1C from pre-intervention to post-intervention was highest in the patients with DAPA + pioglitazone group followed by patients with pioglitazone group then the patients with DAPA group and lowest in patients with metformin group. There was a statistically significant difference between them, (p&lt;0.001). The weight reduction was highest in the patients with DAPA 10mg group followed by patients with metformin group, (p&lt;0.001). The change in SBP, DBP and change in lipid profile (triglyceride and cholesterol, LDL and HDL) from pre-intervention to post-intervention was highest in the patients with DAPA+ pioglitazone group. This change was statistically significant (p&lt;0.001). Conclusions: The combination of pioglitazone and dapagliflozin not only helped in glycemic control but also had reduction in blood pressures, improvement in the lipid profile and caused slight weight reduction. There were no major adverse drug reactions, and no MACE was observed during the study. Hence this combination of pioglitazone and dapagliflozin may reduce the cardiovascular mortality (which needs longer duration study)

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

Improving Age of Information with Interference Problem in Long-Range Wide Area Networks

Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a game-theoretic approach to estimate the time duration of using a spreading factor that ensures on-time data delivery with maximum network utilization. We incorporate the Age of Information (AoI) metric to capture the freshness of information as demanded by the applications. Our proposed approach is validated through simulation experiments, and its applicability is demonstrated for a crop protection system that ensures real-time monitoring and intrusion control of animals in an agricultural field. The simulation and prototype results demonstrate the impact of the number of nodes, AoI metric, and game-theoretic parameters on the performance of the IoT network

Rate-Monotonic Scheduler For LoRa-Based Smart Space Monitoring System

Smart spaces system equipped with sensors to collect data that can be used to generate insights about its environmental conditions. Those collected data is then transmitted to the applications to enhance the comfort, quality of life, and security of the space. Long Range (LoRa) technology provides long distance coverage and consumes low energy which makes it suitable for smart space application. There are six virtual channels to transmit data in LoRa, however network faces the interference problem when nodes transmitted data at the same time. The interference problem makes LoRa less suitable for time-critical applications. To mitigate the interference problem, a spreading factor should be allocated in an optimal way. This paper assigns the spreading factor to the LN using Rate-Monotonic scheduler to ensures data transmission within deadline with minimum energy consumption. To quantify delay in receiving the information, we use the \u27Age of Information\u27 metric. The proposed approach is validated using Network Simulator-3 and results show that it effectively reduces delay and energy and prolongs the network utility