Vertical Semi-Federated Learning for Efficient Online Advertising

As an emerging secure learning paradigm in leveraging cross-silo private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, the 1) restricted applicable scope to overlapped samples and 2) high system challenge of real-time federated serving have limited its application to advertising systems. In this paper, we advocate new learning setting Semi-VFL (Vertical Semi-Federated Learning) as a lightweight solution to utilize all available data (both the overlapped and non-overlapped data) that is free from federated serving. Semi-VFL is expected to perform better than single-party models and maintain a low inference cost. It's notably important to i) alleviate the absence of the passive party's feature and ii) adapt to the whole sample space to implement a good solution for Semi-VFL. Thus, we propose a carefully designed joint privileged learning framework (JPL) as an efficient implementation of Semi-VFL. Specifically, we build an inference-efficient single-party student model applicable to the whole sample space and meanwhile maintain the advantage of the federated feature extension. Novel feature imitation and ranking consistency restriction methods are proposed to extract cross-party feature correlations and maintain cross-sample-space consistency for both the overlapped and non-overlapped data. We conducted extensive experiments on real-world advertising datasets. The results show that our method achieves the best performance over baseline methods and validate its effectiveness in maintaining cross-view feature correlation

Fragments of asthenosphere incorporated in the lithospheric mantle underneath the Subei Basin, eastern China: Constraints from geothermobarometric results and water contents of peridotite xenoliths in Cenozoic basalts

Anhydrous, medium/coarse-grained spinel bearing mantle xenoliths from the Subei Basin, Eastern China have mineral arrangements that reflect low energy geometry. Because of clinopyroxene modal contents, they are grouped into cpx-rich lherzolites (cpx ≥ 14percentage), lherzolites (8 5My, based on modelled H2O solid-solid diffusion rate) the occurrence of the last melting episode. Keywords: Water contents, Fertile mantle, Melting models, Water diffusion, Asthenosphere/lithospher

GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization

Federated Learning (FL) has recently emerged as a promising distributed machine learning framework to preserve clients' privacy, by allowing multiple clients to upload the gradients calculated from their local data to a central server. Recent studies find that the exchanged gradients also take the risk of privacy leakage, e.g., an attacker can invert the shared gradients and recover sensitive data against an FL system by leveraging pre-trained generative adversarial networks (GAN) as prior knowledge. However, performing gradient inversion attacks in the latent space of the GAN model limits their expression ability and generalizability. To tackle these challenges, we propose \textbf{G}radient \textbf{I}nversion over \textbf{F}eature \textbf{D}omains (GIFD), which disassembles the GAN model and searches the feature domains of the intermediate layers. Instead of optimizing only over the initial latent code, we progressively change the optimized layer, from the initial latent space to intermediate layers closer to the output images. In addition, we design a regularizer to avoid unreal image generation by adding a small ${l_1}$ ball constraint to the searching range. We also extend GIFD to the out-of-distribution (OOD) setting, which weakens the assumption that the training sets of GANs and FL tasks obey the same data distribution. Extensive experiments demonstrate that our method can achieve pixel-level reconstruction and is superior to the existing methods. Notably, GIFD also shows great generalizability under different defense strategy settings and batch sizes.Comment: ICCV 202

Achieving Lightweight Federated Advertising with Self-Supervised Split Distillation

As an emerging secure learning paradigm in leveraging cross-agency private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, there are two key challenges in applying it to advertising systems: a) the limited scale of labeled overlapping samples, and b) the high cost of real-time cross-agency serving. In this paper, we propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. We identify that: i) there are massive unlabeled overlapped data available in advertising systems, and ii) we can keep a balance between model performance and inference cost by decomposing the federated model. Specifically, we develop a self-supervised task Matched Pair Detection (MPD) to exploit the vertically partitioned unlabeled data and propose the Split Knowledge Distillation (SplitKD) schema to avoid cross-agency serving. Empirical studies on three industrial datasets exhibit the effectiveness of our methods, with the median AUC over all datasets improved by 0.86% and 2.6% in the local deployment mode and the federated deployment mode respectively. Overall, our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.Comment: Accepted to the Trustworthy Federated Learning workshop of IJCAI2022 (FL-IJCAI22). 6 pages, 3 figures, 3 tables Old title: Semi-Supervised Cross-Silo Advertising with Partial Knowledge Transfe

LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

A few lightweight convolutional neural network (CNN) models have been recently designed for remote sensing object detection (RSOD). However, most of them simply replace vanilla convolutions with stacked separable convolutions, which may not be efficient due to a lot of precision losses and may not be able to detect oriented bounding boxes (OBB). Also, the existing OBB detection methods are difficult to constrain the shape of objects predicted by CNNs accurately. In this paper, we propose an effective lightweight oriented object detector (LO-Det). Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity. The CSA-DRF component optimizes efficiency while maintaining high accuracy. Then, a diagonal support constraint head (DSC-Head) component is designed to detect OBBs and constrain their shapes more accurately and stably. Extensive experiments on public datasets demonstrate that the proposed LO-Det can run very fast even on embedded devices with the competitive accuracy of detecting oriented objects.Comment: 15 page

Revealing two radio active galactic nuclei extremely near PSR J0437−-4715

Newton's gravitational constant $G$ may vary with time at an extremely low level. The time variability of $G$ will affect the orbital motion of a millisecond pulsar in a binary system and cause a tiny difference between the orbital period-dependent measurement of the kinematic distance and the direct measurement of the annual parallax distance. PSR J0437$-$4715 is the nearest millisecond pulsar and the brightest at radio. To explore the feasibility of achieving a parallax distance accuracy of one light-year, comparable to the recent timing result, with the technique of differential astrometry, we searched for compact radio sources quite close to PSR J0437$-$4715. Using existing data from the Very Large Array and the Australia Telescope Compact Array, we detected two sources with flat spectra, relatively stable flux densities of 0.9 and 1.0 mJy at 8.4 GHz and separations of 13 and 45 arcsec. With a network consisting of the Long Baseline Array and the Kunming 40-m radio telescope, we found that both sources have a point-like structure and a brightness temperature of $\geq$10$^7$ K. According to these radio inputs and the absence of counterparts in the other bands, we argue that they are most likely the compact radio cores of extragalactic active galactic nuclei rather than Galactic radio stars. The finding of these two radio active galactic nuclei will enable us to achieve a sub-pc distance accuracy with the in-beam phase-referencing very-long-baseline interferometric observations and provide one of the most stringent constraints on the time variability of $G$ in the near future.Comment: 9 pages, 3 tables, 3 figures. Accepted for publication in MNRA

Učinak glicerola i glukoze na povećanje biomase, udjela lipida i topljivih ugljikohidrata u miksotrofnoj kulturi alge Chlorella vulgaris

Biodiesel-derived glycerol is a promising substrate for mixotrophic cultivation of oleaginous microalgae, which can also reduce the cost of microalgal biodiesel. The objective of this study is to investigate the potential of using glycerol and glucose as a complex carbon substrate to produce microalgal biomass and biochemical components, such as photosynthetic pigments, lipids, soluble carbohydrates and proteins by Chlorella vulgaris. The results show that C. vulgaris can utilize glycerol as a sole carbon substrate, but its effect is inferior to that of the mixture of glycerol and glucose. The effect of glycerol and glucose could enhance the algal cell growth rate, biomass content and volumetric productivity, and overcome the lower biomass production on glycerol as the sole organic carbon source in mixotrophic culture medium. The utilization of complex organic carbon substrate can stimulate the biosynthesis of lipids and soluble carbohydrates as the raw materials for biodiesel and bioethanol production, and reduce the anabolism of photosynthetic pigments and proteins. This study provides a promising niche for reducing the overall cost of biodiesel and bioethanol production from microalgae as it investigates the by-products of algal biodiesel production and algal cell hydrolysis as possible raw materials (lipids and carbohydrates) and organic carbon substrates (soluble carbohydrates and glycerol) for mixotrophic cultivation of microalgae.Glicerol dobiven iz biodizela može se upotrijebiti za uzgoj miksotrofnih mikroalgi iz kojih se proizvodi ulje, te za smanjenje troškova proizvodnje biodizela. Svrha je ovoga rada bila ispitati mogućnost primjene glicerola i glukoze u složenoj hranjivoj podlozi za uzgoj alge Chlorella vulgaris, te proizvodnju biomase i biokemijskih sastojaka, kao što su fotosintetski pigmenti, lipidi, topljivi ugljikohidrati i proteini. Rezultati potvrđuju da se alga Chlorella vulgaris može uzgojiti na glicerolu kao jedinom izvoru ugljika, iako su bolji rezultati postignuti uzgojem na podlozi s glicerolom i glukozom. Stopa rasta, prinos biomase i volumetrijska produktivnost alge povećani su primjenom podloge s glicerolom i glukozom, u usporedbi s podlogom koja sadržava samo glukozu kao organski izvor ugljika. Uporabom složene hranjive podloge može se ubrzati biosinteza lipida i topljivih ugljikohidrata (sirovina za proizvodnju biodizela i bioetanola), te usporiti sinteza fotosintetskih pigmenata i proteina. U radu je ispitana mogućnost primjene nusproizvoda dobivenih proizvodnjom biodizela i hidrolizom algi te podloga s organskim izvorima ugljika (topljivi ugljikohidrati i glicerol) kao sirovina (izvor lipida i ugljikohidrata) za proizvodnju miksotrofnih mikroalgi, radi smanjenja ukupnih troškova proizvodnje biodizela i bioetanola