arXiv subjects

Zihao Zhao

Publications and source records attributed to Zihao Zhao.

65 records · Page 4Linked to original sources

arXiv2022

Towards Efficient Communications in Federated Learning: A Contemporary Survey

In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure with privacy protection mechanisms, which can achieve multi-party collaborative computing without revealing the original data. However, in practice, FL faces a variety of challenging communication problems. This review seeks to elucidate the relationship between these communication issues by methodically assessing the development of FL communication research from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Second, we have collated FL communications-related papers and described the overall development trend of the field based on their logical relationship. Ultimately, we discuss the future directions of research for communications in FL.

cs.DC↗

arXiv2022

PandaDB: Understanding Unstructured Data in Graph Database

Unstructured data(e.g., images, videos, PDF files, etc.) contain semantic information, for example, the facial feature of a person and the plate number of a vehicle. There could be semantic relationships between data items which are not explicitly represented. For example, a person's face may appear in two irrelevant photos. Also, much information is represented as structured data(e.g., the person's name and age). End-users prefer to query the semantic information from unstructured data together with structured data based on the potential relationships among them. However, due to the lack of a unified database system for structured and unstructured data, developers have to comprise multiple systems and runtime together to answer these queries. In this work, we build an open-source graph database named PandaDB to consistently manage and query structured and unstructured data. We first introduce a graph data model to manage structured and unstructured data, then propose a new query language to understand the semantics of the unstructured data in the graph. Next, we develop a new cost model and related query optimization techniques to speed up the unstructured data processing pipeline. Finally, we optimize the unstructured data storage and provide the index to speed up the query processing over unstructured data. PandaDB is widely used in industrial applications like FinTech, Knowledge Graph, and Recommendation System. The results show PandaDB can support a large scale of unstructured data query processing in a graph.

cs.DB↗

arXiv2022

Deep Leakage from Model in Federated Learning

Distributed machine learning has been widely used in recent years to tackle the large and complex dataset problem. Therewith, the security of distributed learning has also drawn increasing attentions from both academia and industry. In this context, federated learning (FL) was developed as a "secure" distributed learning by maintaining private training data locally and only public model gradients are communicated between. However, to date, a variety of gradient leakage attacks have been proposed for this procedure and prove that it is insecure. For instance, a common drawback of these attacks is shared: they require too much auxiliary information such as model weights, optimizers, and some hyperparameters (e.g., learning rate), which are difficult to obtain in real situations. Moreover, many existing algorithms avoid transmitting model gradients in FL and turn to sending model weights, such as FedAvg, but few people consider its security breach. In this paper, we present two novel frameworks to demonstrate that transmitting model weights is also likely to leak private local data of clients, i.e., (DLM and DLM+), under the FL scenario. In addition, a number of experiments are performed to illustrate the effect and generality of our attack frameworks. At the end of this paper, we also introduce two defenses to the proposed attacks and evaluate their protection effects. Comprehensively, the proposed attack and defense schemes can be applied to the general distributed learning scenario as well, just with some appropriate customization.

cs.LG↗

arXiv2022

The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture Recognition

Action recognition is an exciting research avenue for artificial intelligence since it may be a game changer in the emerging industrial fields such as robotic visions and automobiles. However, current deep learning faces major challenges for such applications because of the huge computational cost and the inefficient learning. Hence, we develop a novel brain-inspired Spiking Neural Network (SNN) based system titled Spiking Gating Flow (SGF) for online action learning. The developed system consists of multiple SGF units which assembled in a hierarchical manner. A single SGF unit involves three layers: a feature extraction layer, an event-driven layer and a histogram-based training layer. To demonstrate the developed system capabilities, we employ a standard Dynamic Vision Sensor (DVS) gesture classification as a benchmark. The results indicate that we can achieve 87.5% accuracy which is comparable with Deep Learning (DL), but at smaller training/inference data number ratio 1.5:1. And only a single training epoch is required during the learning process. Meanwhile, to the best of our knowledge, this is the highest accuracy among the non-backpropagation algorithm based SNNs. At last, we conclude the few-shot learning paradigm of the developed network: 1) a hierarchical structure-based network design involves human prior knowledge; 2) SNNs for content based global dynamic feature detection.

cs.CV↗

arXiv2022

SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications

Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency of high-dimensional models and addressing bias caused by local updates. However, most of FL algorithms are either based on reliable communications or assume fixed and known unreliability characteristics. In practice, networks could suffer from dynamic channel conditions and non-deterministic disruptions, with time-varying and unknown characteristics. To this end, in this paper we propose a sparsity enabled FL framework with both communication efficiency and bias reduction, termed as SAFARI. It makes novel use of a similarity among client models to rectify and compensate for bias that is resulted from unreliable communications. More precisely, sparse learning is implemented on local clients to mitigate communication overhead, while to cope with unreliable communications, a similarity-based compensation method is proposed to provide surrogates for missing model updates. We analyze SAFARI under bounded dissimilarity and with respect to sparse models. It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications. Implementations and evaluations on CIFAR-10 dataset validate the effectiveness of SAFARI by showing that it can achieve the same convergence speed and accuracy as FedAvg with perfect communications, with up to 80% of the model weights being pruned and a high percentage of client updates missing in each round.

cs.DC↗

arXiv2021

Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks

Deep neural networks (DNNs) have been proven to be effective in solving many real-life problems, but its high computation cost prohibits those models from being deployed to edge devices. Pruning, as a method to introduce zeros to model weights, has shown to be an effective method to provide good trade-offs between model accuracy and computation efficiency, and is a widely-used method to generate compressed models. However, the granularity of pruning makes important trade-offs. At the same sparsity level, a coarse-grained structured sparse pattern is more efficient on conventional hardware but results in worse accuracy, while a fine-grained unstructured sparse pattern can achieve better accuracy but is inefficient on existing hardware. On the other hand, some modern processors are equipped with fast on-chip scratchpad memories and gather/scatter engines that perform indirect load and store operations on such memories. In this work, we propose a set of novel sparse patterns, named gather-scatter (GS) patterns, to utilize the scratchpad memories and gather/scatter engines to speed up neural network inferences. Correspondingly, we present a compact sparse format. The proposed set of sparse patterns, along with a novel pruning methodology, address the load imbalance issue and result in models with quality close to unstructured sparse models and computation efficiency close to structured sparse models. Our experiments show that GS patterns consistently make better trade-offs between accuracy and computation efficiency compared to conventional structured sparse patterns. GS patterns can reduce the runtime of the DNN components by two to three times at the same accuracy levels. This is confirmed on three different deep learning tasks and popular models, namely, GNMT for machine translation, ResNet50 for image recognition, and Japser for acoustic speech recognition.

cs.LG↗

arXiv2021

Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation

Recommender system usually suffers from severe popularity bias -- the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such skewed distribution may result from the users' conformity to the group, which deviates from reflecting users' true preference. Existing efforts for tackling this issue mainly focus on completely eliminating popularity bias. However, we argue that not all popularity bias is evil. Popularity bias not only results from conformity but also item quality, which is usually ignored by existing methods. Some items exhibit higher popularity as they have intrinsic better property. Blindly removing the popularity bias would lose such important signal, and further deteriorate model performance. To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity. Although important, it is quite challenging as we lack an explicit signal to differentiate the two factors of popularity bias. In this paper, we propose to leverage temporal information as the two factors exhibit quite different patterns along the time: item quality revealing item inherent property is stable and static while conformity that depends on items' recent clicks is highly time-sensitive. Correspondingly, we further propose a novel Time-aware DisEntangled framework (TIDE), where a click is generated from three components namely the static item quality, the dynamic conformity effect, as well as the user-item matching score returned by any recommendation model. Lastly, we conduct interventional inference such that the recommendation can benefit from the benign popularity bias while circumvent the harmful one. Extensive experiments on three real-world datasets demonstrated the effectiveness of TIDE.

cs.IR↗

arXiv2021

Communication Efficient Federated Learning with Adaptive Quantization

Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy preserving measures and great potentials in some distributed but privacy-sensitive applications like finance and health. However, high communication overloads for transmitting high-dimensional networks and extra security masks remains a bottleneck of FL. This paper proposes a communication-efficient FL framework with Adaptive Quantized Gradient (AQG) which adaptively adjusts the quantization level based on local gradient's update to fully utilize the heterogeneousness of local data distribution for reducing unnecessary transmissions. Besides, the client dropout issues are taken into account and the Augmented AQG is developed, which could limit the dropout noise with an appropriate amplification mechanism for transmitted gradients. Theoretical analysis and experiment results show that the proposed AQG leads to 25%-50% of additional transmission reduction as compared to existing popular methods including Quantized Gradient Descent (QGD) and Lazily Aggregated Quantized (LAQ) gradient-based method without deteriorating convergence properties. Particularly, experiments with heterogenous data distributions corroborate a more significant transmission reduction compared with independent identical data distributions. Meanwhile, the proposed AQG is robust to a client dropping rate up to 90% empirically, and the Augmented AQG manages to further improve the FL system's communication efficiency with the presence of moderate-scale client dropouts commonly seen in practical FL scenarios.

cs.DC↗

arXiv2020

Passively mode-locked thulium-doped all-fiber laser based on low V number fiber bending

This paper describes a mode locking technique based on enhanced polarization dependent loss (PDL). The method utilizes a bent single mode fiber (SMF28) coil to induce sufficient PDL at 2 \(μ\)m wavelength region. Significant PDL in SMF28 coils is enabled since the light is much more weakly guided. A passively mode-locked thulium doped all-fiber laser is demonstrated using this simple device with polarization controllers as mode locker. The results indicate a moderate amount of 1 dB is sufficient to initiate and sustain stable mode-locking operation. We believe, to the best of our knowledge, this is the first demonstration of mode-locked fiber laser fully based on polarization dependent property of bent fiber section.

physics.optics↗

arXiv2019

Exploring Evolving Plants as Interacting Particles in a Randomly Generated Heterogeneous Environment

We model evolution of plants in a world, made up of different locations, with multiple environments (mutually exclusive and collectively exhaustive subsets of locations). Each environment (landmass) has temperature, rainfall, and other attributes that directly affect plant growth and reproduction. Each plant has preferences for environment attributes. Depending on how suitable the environment is to the plants, seeds are released or death occurs. With every reproductive cycle, genetic mutations occur. To model competition, plants in compete for survival, and success is stochastically dependent on environmental fitness. Our model determines whether and how evolution occurs, and how the attributes of plants change and possibly converge over time in relation to the attributes of the environment.

q-bio.PE↗

arXiv2018

Spontaneous laser line sweeping in bi-directional ring thulium fiber laser

We report a phenomenon of self-sweeping in a bi-directional ring thulium-doped fiber laser, for the first time. The laser is spontaneously sweeping in both directions at a rate up to 0.2 nm/s with 15 nm sweeping range in 1.95 μm wavelength region. The laser output is switchable between two different working modes: periodical spontaneous laser line sweeping with generation of microsecond pulses in time domain; or static central wavelength with amplitude modulated temporally.

physics.optics↗