Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), has recently gained increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to build better machine learning models while preserving user privacy. Current works in VFL concentrate on developing a specific protection or attack mechanism for a particular VFL algorithm. In this work, we propose an evaluation framework that formulates the privacy-utility evaluation problem. We then use this framework as a guide to comprehensively evaluate a broad range of protection mechanisms against most of the state-of-the-art privacy attacks for three widely-deployed VFL algorithms. These evaluations may help FL practitioners select appropriate protection mechanisms given specific requirements. Our evaluation results demonstrate that: the model inversion and most of the label inference attacks can be thwarted by existing protection mechanisms; the model completion (MC) attack is difficult to be prevented, which calls for more advanced MC-targeted protection mechanisms. Based on our evaluation results, we offer concrete advice on improving the privacy-preserving capability of VFL systems.

Oakland, California – When Silicon Valley chipmaker Marvell learned that one of its chips was found in a Russian surveillance drone recovered in 2016, it set out to investigate how that came to be. The chip, which costs less than $2, was shipped in 2009 to a distributor in Asia, which sold it to another broker in Asia, which later went out of business. "We couldn't trace it any further," Marvell Technology Group Chief Operations Officer Chris Koopmans said in a recent interview. Years later, it reappeared in the drone recovered in Lithuania. Marvell's experience is one of myriad examples of how chipmakers lack ability to track where many of their lower-end products end up, executives and experts said.

These days, artificial intelligence is showing up in all sorts of new services. There's a reason: The use of AI is growing fast, and businesses putting it to good use are unlocking efficiencies and delivering better experiences to their customers. According to tech researcher IDC, global spending on AI is expected to have increased more than 12% this year and to top $156 billion. That's impressive given the current state of world affairs, and AI is expected to continue its expansion for the foreseeable future. If investing in the artificial intelligence trend for the long-haul is your goal, Dynatrace (NYSE:DT), Marvell Technology Group (NASDAQ:MRVL), and Medallia (NYSE:MDLA) stocks are worth serious looks.

The past year has been inundated by a wave of consolidation among chipmakers. Throughout 2019, billions of dollars changed hands as industry giants attempted to fill gaps in their product lines and bolster their positions in some of the world's most competitive markets including data center connectivity, artificial intelligence (AI), and 5G networking. Here's a rundown of 2019's five hungriest hippos and the silicon morsels they gobbled up or tossed aside. Intel closed out 2019 by betting big on AI with the purchase of AI startup Habana Labs for $2 billion. The purchase is aimed at cementing Intel's position in the highly competitive data center processor space, which the company predicts will be worth more than $25 billion by 2024.

SANTA CLARA, Calif. and DENVER, Colo., Nov. 19, 2019 -- Marvell today announced the availability of NVIDIA GPU support on its ThunderX family of Arm-based server processors. Following NVIDIA's June announcement to bring CUDA to the Arm architecture, Marvell has collaborated with NVIDIA to port its CUDA-X AI and HPC libraries, GPU-accelerated AI frameworks and software development tools to the ThunderX platform. The computational performance and memory bandwidth of ThunderX2, Marvell's latest 64-bit Armv8-A based server processor, combined with the parallel processing capabilities of NVIDIA GPUs provide a compelling path to energy-efficient exascale computing. Artificial intelligence (AI) and machine learning (ML) continue to become essential technology components to data center server requirements at the cloud and network edge. To address these evolving AI and ML workloads, as well as the most challenging and complex problems in science and research, supercomputers need processors that are optimized to provide cutting-edge throughput, application latency and power.