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Talend Recognized in CRN's Big Data 100 List for Third Consecutive Year - NASDAQ.com

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REDWOOD CITY, Calif., May 22, 2018 (GLOBE NEWSWIRE) -- Talend (NASDAQ: TLND), a leader in cloud data integration solutions, has been named to the 2018 CRN Big Data 100 list, a brand of The Channel Company. This annual list recognizes vendors that have demonstrated an ability to innovate in bringing to market products and services that help businesses work with one of the most dynamic, fastest growing segments of the IT industry - Big Data. As a result of Talend's inclusion in the CRN Big Data 100, Solutions Review selected Talend Open Studio for Data Integration and Talend Cloud among its list of "7 Data Integration Tools We Recommend". The data explosion in recent years has fueled a vibrant big data technology industry. Businesses need innovative products and services to capture, integrate, manage and analyze the massive volumes of data they are grappling with every day.


TPG Growth Invests in C3 IoT and its Internet of Things Platform

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REDWOOD CITY, Calif., FORT WORTH, Texas, and SAN FRANCISCO -- TPG Growth announced it has invested in C3 IoT, reinforcing its position in the Internet of Things (IoT) market. According to the company, C3 IoT applies the sciences of big data, advanced analytics, and machine learning to enable smart business processes. The C3 IoT Platform is a development environment that allows rapid design, development, and deployment of enterprise-scale big data and IoT applications. The platform aggregates and integrates large volumes of disparate data -- from sensors on equipment and machinery; enterprise operational systems such as financial transactional systems and enterprise resource planning (ERP) systems; and third-party sources such as weather data -- and delivers machine learning-based predictive analytics to improve operational efficiencies, enhance customer engagement, and differentiate products and services. C3 IoT provides pre-built software as a service (SaaS) applications, including predictive maintenance, fraud detection, energy management, and network sensor health for organizations in manufacturing, oil and gas, health care, retail, aerospace, transportation, telecommunications, and the public sector.


Free-riders in Federated Learning: Attacks and Defenses

arXiv.org Machine Learning

Free-riders in Federated Learning: Attacks and Defenses Jierui Lin, Min Du, and Jian Liu University of California, Berkeley Abstract--Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages the parameter server to generate a global model by aggregating the locally submitted gradient updates at each round. Although the incentive model for federated learning has not been fully developed, it is supposed that participants are able to get rewards or the privilege to use the final global model, as a compensation for taking efforts to train the model. Therefore, a client who does not have any local data has the incentive to construct local gradient updates in order to deceive for rewards. In this paper, we are the first to propose the notion of free rider attacks, to explore possible ways that an attacker may construct gradient updates, without any local training data. Furthermore, we explore possible defenses that could detect the proposed attacks, and propose a new high dimensional detection method called STD-DAGMM, which particularly works well for anomaly detection of model parameters. We extend the attacks and defenses to consider more free riders as well as differential privacy, which sheds light on and calls for future research in this field. I NTRODUCTION F EDERA TED learning [1], [2], [3] has been proposed to facilitate a joint model training leveraging data from multiple clients, where the training process is coordinated by a parameter server. In the whole process, clients' data stay local, and only model parameters are communicated among clients through the parameter server. A typical training iteration works as follows. First, the parameter server sends the newest global model to each client. Then, each client locally updates the model using local data and reports updated gradients to the parameter server. Finally, the server performs model aggregation on all submitted local updates to form a new global model, which has better performance than models trained using any single client's data.


Guavus Acquires SQLstream

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SAN JOSE, Calif., Jan. 25, 2019 -- Guavus, a Thales company and pioneer in Artificial Intelligence (AI)-based analytics, announced today that it has acquired SQLstream, a real-time streaming analytics company based in San Francisco, CA. The acquisition enables Guavus to expand its offering, providing communications service providers (CSPs) and Industrial Internet of Things (IIoT) customers access – at the network edge – to real-time, cloud-enabled streaming analytics to address their growing big data needs. "With our integrated solutions, CSPs to IIoT customers will be able to take advantage of something that's radically different as we deliver AI-powered analytics from the network edge to the network core. With this solution, our customers can now analyze their operational, customer, and business data anywhere in the network in real time, without manual intervention, so they can make better decisions, provide smarter new services, and reduce their costs," said Guavus CEO, Faizel Lakhani. "In a world facing exponential growth in the volume of data coming from increasingly connected network devices and IIoT-based sensors, the inclusion of SQLstream's industry-leading technology opens up huge new opportunities for our customers and our partners. Their disruptive technology allows customers to interactively inspect and curate streaming data for analytics at the edge. We're excited to have the SQLstream team onboard," said Lakhani.


AI and Big Data: The Next Frontier of Fintech

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Data analytics is becoming a cornerstone of the financial industry, with startups and established financial service firms looking to give investors clearer guidance with information collected and captured from multiple sources. Advances in machine learning and artificial intelligence (AI) in particular are providing greater insights and better customer experiences. AI-powered data analytics not only captures vast amounts of data in real-time, but also helps users understand how different data points relate to each other, providing insights that might otherwise be lost. Faced with a breakdown in brand loyalty as younger customers prioritize user experience, financial services are now racing to leverage data-driven cognitive technologies. Cambridge, MA-based Kensho, which recently received 58 million in funding from Goldman Sachs, San Francisco-based Alphasense, backed by Tribeca Venture Partners, and Toronto-based Bigterminal are some of the fintech players leveraging AI.