With more regulations tackling the protection of users' privacy-sensitive data in recent years, access to such data has become increasingly restricted. A new decentralized training paradigm, known as ...Federated Learning (FL), enables multiple clients located at different geographical locations to learn a machine learning model collaboratively without sharing their data. While FL has recently emerged as a promising solution to preserve users' privacy, this new paradigm's potential security implications may hinder its widespread adoption. The existing FL protocols exhibit new unique vulnerabilities that adversaries can exploit to compromise the trained model. FL is often preferred in learning environments where security and privacy are the key concerns. Therefore, it is crucial to raise awareness of the consequences resulting from the new threats to FL systems. To date, the security of traditional machine learning systems has been widely examined. However, many open challenges and complex questions are still surrounding FL security. In this paper, we bridge the gap in FL literature by providing a comprehensive survey of the unique security vulnerabilities exposed by the FL ecosystem. We highlight the vulnerabilities sources, key attacks on FL, defenses, as well as their unique challenges, and discuss promising future research directions towards more robust FL.
The solid-state lighting is revolutionizing the indoor illumination. Current incandescent and fluorescent lamps are being replaced by the LEDs at a rapid pace. Apart from extremely high energy ...efficiency, the LEDs have other advantages such as longer lifespan, lower heat generation, and improved color rendering without using harmful chemicals. One additional benefit of LEDs is that they are capable of switching to different light intensity at a very fast rate. This functionality has given rise to a novel communication technology (known as visible light communication-VLC) where LED luminaires can be used for high speed data transfer. This survey provides a technology overview and review of existing literature of visible light communication and sensing. This paper provides a detailed survey of 1) visible light communication system and characteristics of its various components such as transmitter and receiver; 2) physical layer properties of visible light communication channel, modulation methods, and MIMO techniques; 3) medium access techniques; 4) system design and programmable platforms; and 5) visible light sensing and application such as indoor localization, gesture recognition, screen-camera communication, and vehicular networking. We also outline important challenges that need to be addressed in order to design high-speed mobile networks using visible light communication.
Clustering algorithms are a class of unsupervised machine learning (ML) algorithms that feature ubiquitously in modern data science, and play a key role in many learning-based application pipelines. ...Recently, research in the ML community has pivoted to analyzing the fairness of learning models, including clustering algorithms. Furthermore, research on fair clustering varies widely depending on the choice of clustering algorithm, fairness definitions employed, and other assumptions made regarding models. Despite this, a comprehensive survey of the field does not exist. In this paper, we seek to bridge this gap by categorizing existing research on fair clustering, and discussing possible avenues for future work. Through this survey, we aim to provide researchers with an organized overview of the field, and motivate new and unexplored lines of research regarding fairness in clustering.
Many smartphone applications, e.g., file backup, are intrinsically delay-tolerant so that data processing and transfer can be delayed to reduce smartphone battery usage. In the literature, these ...energy-delay tradeoff issues have been addressed independently in the forms of Dynamic Voltage and Frequency Scaling (DVFS) problems and network selection problems when smartphones have multiple wireless interfaces. In this paper, we jointly optimize the CPU speed and network speed to determine how much more energy can be saved through the joint optimization when applications can tolerate delays. We propose a dynamic speed scaling scheme called SpeedControl that jointly adjusts the processing and networking speeds using four controls: application scheduling, CPU speed control, wireless interface selection, and transmit power control. Through invoking the "Lyapunov drift-plus-penalty" technique, the scheme is demonstrated to be near optimal because it substantially reduces energy consumption for a given delay constraint. This paper is the first to reveal the energy-delay tradeoff relationship from a holistic perspective for smartphones with multiple wireless interfaces, DVFS, and multitasking capabilities. The trace-driven simulations based on real measurements of CPU power, network power, WiFi/3G throughput, and CPU workload demonstrate that SpeedControl can reduce battery usage by more than 42% through trading a 10 minutes delay when compared with the same delay in existing schemes; moreover, this energy conservation level increases as the WiFi coverage extends.
The Ethernet switch is a primary building block for today's enterprise networks and data centers. As network technologies converge upon a single Ethernet fabric, there is ongoing pressure to improve ...the performance and efficiency of the switch while maintaining flexibility and a rich set of packet processing features. The OpenFlow architecture aims to provide flexibility and programmable packet processing to meet these converging needs. Of the many ways to create an OpenFlow switch, a popular choice is to make heavy use of ternary content addressable memories (TCAMs). Unfortunately, TCAMs can consume a considerable amount of power and, when used to match flows in an OpenFlow switch, put a bound on switch latency. In this paper, we propose enhancing an OpenFlow Ethernet switch with per-port packet prediction circuitry in order to simultaneously reduce latency and power consumption without sacrificing rich policy-based forwarding enabled by the OpenFlow architecture. Packet prediction exploits the temporal locality in network communications to predict the flow classification of incoming packets. When predictions are correct, latency can be reduced, and significant power savings can be achieved from bypassing the full lookup process. Simulation studies using actual network traces indicate that correct prediction rates of 97% are achievable using only a small amount of prediction circuitry per port. These studies also show that prediction circuitry can help reduce the power consumed by a lookup process that includes a TCAM by 92% and simultaneously reduce the latency of a cut-through switch by 66%.
Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). Although mobility is an inherent property of wireless networks, there has been no prior work studying the ...performance of collaborative spectrum sensing under attacks in mobile CRNs. Existing solutions based on user trust for secure collaborative spectrum sensing cannot be applied to mobile scenarios, since they do not consider the location diversity of the network, thus over penalize honest users who are at bad locations with severe path-loss. In this paper, we propose to use two trust parameters, location reliability and malicious intention (LRMI), to improve both malicious user detection and primary user detection in mobile CRNs under attack. Location reliability reflects path-loss characteristics of the wireless channel and malicious intention captures the true intention of secondary users, respectively. We propose a primary user detection method based on location reliability (LR) and a malicious user detection method based on LR and Dempster-Shafer (D-S) theory. Simulations show that mobility helps train location reliability and detect malicious users based on our methods. Our proposed detection mechanisms based on LRMI significantly outperforms existing solutions. In comparison to the existing solutions, we show an improvement of malicious user detection rate by 3 times and primary user detection rate by 20% at false alarm rate of 5%, respectively.
Acoustic signal analysis has been employed in various medical devices. However, studies involving cough sound analysis to screen the potential pulmonary tuberculosis (PTB) suspects are very few. The ...main objective of this cross-sectional validation study was to develop and validate the Swaasa AI platform to screen and prioritize at risk patients for PTB based on the signature cough sound as well as symptomatic information provided by the subjects. The voluntary cough sound data was collected at Andhra Medical College-India. An Algorithm based on multimodal convolutional neural network architecture and feedforward artificial neural network (tabular features) was built and validated on a total of 567 subjects, comprising 278 positive and 289 negative PTB cases. The output from these two models was combined to detect the likely presence (positive cases) of PTB. In the clinical validation phase, the AI-model was found to be 86.82% accurate in detecting the likely presence of PTB with 90.36% sensitivity and 84.67% specificity. The pilot testing of model was conducted at a peripheral health care centre, RHC Simhachalam-India on 65 presumptive PTB cases. Out of which, 15 subjects truly turned out to be PTB positive with a positive predictive value of 75%. The validation results obtained from the model are quite encouraging. This platform has the potential to fulfil the unmet need of a cost-effective PTB screening method. It works remotely, presents instantaneous results, and does not require a highly trained operator. Therefore, it could be implemented in various inaccessible, resource-poor parts of the world.
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