Reviewerly/exHarmony huggingface dataset:
Fast Tip Selection for Burst Message Arrivals on A DAG-based Blockchain Processing Node at Edge

With the rapid evolution of blockchain technology, a clear trend is that new blockchain systems (e.g., IOTA) tend to use a Directed Acyclic Graph (DAG) rather a chain structure to organize ledger records. Such a DAG-based blockchain system shows higher scalability as multiple locations are available in the ledger for new message attachment. To decide an attachment location, a popular type of tip selection algorithms follow an approach using weighted random walks on the DAG ledger. In a burst message arrival scenario, however, a processing node deployed at edge using such a method may become a bottleneck because sequentially repeating random walks significantly increases processing delay. In this paper, we propose a new tip selection algorithm for the burst message arrival scenario on an edge node. Our solution abandons the weighted random walk approach, instead, with similar efforts we transfer to calculate in advance the tip selection probability distribution of the DAG ledger. Such a new scheme reduces tip selection to a probability distribution sampling task, which can be done extremely fast. We implement our solution and demonstrate the benefits of our approach by comparing with the random walk approach. We believe our attempt can effectively mitigate the congestion at the edge node and inspire tip selection algorithm design with a new vision for DAG-based blockchain systems.
Interplay between Distributed AI Workflow and URLLC

Distributed artificial intelligence (AI) has recently accomplished tremendous breakthroughs in various communication services, ranging from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wireless connected devices, random channel fluctuations, and the incumbent services simultaneously running on the same network affect the performance of distributed learning. In this paper, we investigate the interplay between distributed AI workflow and ultra-reliable low latency communication (URLLC) services running concurrently over a network. Using 3GPP compliant simulations in a factory automation use case, we show the impact of various distributed AI settings (e.g., model size and the number of participating devices) on the convergence time of distributed AI and the application layer performance of URLLC. Unless we leverage the existing 5G-NR quality of service handling mechanisms to separate the traffic from the two services, our simulation results show that the impact of distributed AI on the availability of the URLLC devices is significant. Moreover, with proper setting of distributed AI (e.g., proper user selection), we can substantially reduce network resource utilization, leading to lower latency for distributed AI and higher availability for the URLLC users. Our results provide important insights for future 6G and AI standardization.
Accelerate State Sharing of Network Function with RDMA

State sharing can help network functions (NFs) provide support for clustered deployment and minimize the jitter caused by elastic scaling, one of the central futures of NFV. But the network overhead of remote access and the dynamically changing workload restrict the performance of the state sharing framework. The state-of-the-art state sharing frameworks mainly use the affinity strategy to migrate the target state from another node to itself. This strategy is unsuitable for asymmetric routing scenario because multiple nodes will access the same state. This paper presents RedKV, a fast and flexible Remote Direct Memory Access (RDMA) Enhanced Distributed Key-Value store that supports low-latency state sharing both in symmetric and asymmetric routing scenarios. RedKV has two unconventional designs: First, it scatters states over cluster nodes without affinity strategy and uses a unified addressing (UA) space to hide the complexity of state access. Second, it accelerates the operation with RDMA and optimizes the process according to the characteristics of RDMA and NF to mitigate the cost of remote access. Our evaluation shows that RedKV can help network functions share states with an added latency overhead of 2.26µs, outperforming state-of-art solutions by 4.6 times. The pause time caused by the scaling event has also been reduced by 81.5%.
Reinforcement Learning-Based Network Slice Resource Allocation for Federated Learning Applications

This paper addresses a resource allocation strategy for network slices, where each network slice supports a different federated learning task. A slice is established when a new federated learning model needs to be trained and is released once the training is complete. The goal is to minimize the average network slice holding time while also providing fairness between slice tenants and improving network efficiency. We propose a reinforcement learning-based strategy to periodically reallocate resources according to the current state of each federated learning task. We offer two reinforcement learning models. The first model achieves more stable performance and considers correlations between tasks, while the second model utilizes fewer parameters and is more robust to varying number of tasks. Both approaches have better performance than baseline heuristic methods. We also propose a method to alleviate the effect of various resources scales to make the training stable.
Dynamic Control of Data-Intensive Services Over Edge Computing Networks

Next-generation distributed computing networks (e.g., edge and fog computing) enable the efficient delivery of delay-sensitive, compute-intensive applications by facilitating access to computation resources in close proximity to end users. Many of these applications (e.g., augmented/virtual reality) are also data-intensive: in addition to user-specific (live) data streams, they require access to shared (static) digital objects (e.g., image database) to complete the required processing tasks. When required objects are not available at the servers hosting the associated service functions, they must be fetched from other edge locations, incurring additional communication cost and latency. In such settings, overall service delivery performance shall benefit from jointly optimized decisions around (i) routing paths and processing locations for live data streams, together with (ii) cache selection and distribution paths for associated digital objects. In this paper, we address the problem of dynamic control of data-intensive services over edge cloud networks. We characterize the network stability region and design the first throughput-optimal control policy that coordinates processing and routing decisions for both live and static data-streams. Numerical results demonstrate the superior performance (e.g., throughput, delay, and resource consumption) obtained via the novel multi-pipeline flow control mechanism of the proposed policy, compared with state-of-the-art algorithms that lack integrated stream processing and data distribution control.
Subjective Quality of Experience Assessment in Mobile Cloud Games

The rise of mobile cloud gaming (MCG) has necessitated understanding its impact on mobile network design and deployment for end users' QoE maximization. MCG is a dynamic service that requires stringent quality from network operators. Therefore, this paper investigates the subjective QoE of MCG over mobile networks played on smartphones. We conducted subjective tests (N=31); our results indicate that MCG is affected differently by QoS attributes such as packet loss (PL), round trip time (RTT) and jitter compared to cloud games and online mobile games. We identify that RTT values above 100 milliseconds significantly impact users' QoE, measured via the mean opinion score (MOS). Further, lower RTT values with high PL; and higher RTT values with low PL cause a strong negative effect on MOS. Lastly, bursty jitter seems to affect the MOS, while random jitter does not significantly impact MOS.
Fast Secure Aggregation for Privacy-Preserving Federated Learning

Federated learning (FL) is a new distributed learning paradigm, in which the clients cooperate to conduct the global model without exposing local private data. However, existing privacy inference attacks on FL show that adversaries can still reverse the training data from the submitted model updates. Recently, secure aggregation has been proposed and integrated into the FL framework, which effectively guarantees privacy through various cryptographic techniques, unfortunately at the cost of a large amount of communication and computation. In this paper, we propose a highly efficient secure aggregation scheme, Fast-Aggregate, which significantly reduces the communication and computation overhead while ensuring data privacy and robustness against clients' dropout. Firstly, Fast-Aggregate employs a multi-group regular graph for efficient secure aggregation to boost data parallelism. Secondly, we leverage polynomial multi-point evaluation and fast Lagrange interpolation methods to handle clients' dropout as well as reduce computational complexity. Finally, we adopt an additive mask to guarantee clients' privacy. Riding on the capabilities of Fast-Aggregate, we achieve the secure aggregation overhead of O (N log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> ), as opposed to O (N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) in the state-of-the-art works. Besides, Fast-Aggregate improves training speed without loss of model quality and provides flexibility to deal with client corruption at the same time.
A NTRU-Based Access Authentication Scheme for Satellite Terrestrial Integrated Network

The Satellite Terrestrial Integrated Network(STIN) has become an indispensable part of the future network. However, the limited resources, long delay communications and highly exposed channels of SGIN are vulnerable to network attacks, which make the access authentication scheme as the first line of defense in network security. Most of the existing access authentication schemes are based on discrete logarithm and large integer factorization problems, which cannot resist quantum attacks, and the number of interactions between entities are too large. Therefore, we propose a lightweight and certificateless anonymous access authentication scheme based on lattice to solve this problem. We introduce Number Theory Research Unit (NTRU) scheme into the key generation process, to improve the utilization rate of equipment resources and ensure the legitimacy of the communication entity. The performance evaluation results demonstrate that our scheme only needs twice satellite-ground interactions to complete mutual authentication.
Secure and Efficient Traffic Obfuscation for Smart Home

Recent research has shown that hackers can ef-ficiently infer sensitive user activities only by observing the network traffic of smart home devices. To protect users' privacy, researchers have designed several traffic obfuscation methods. However, existing methods usually consume high bandwidth or provide weak privacy protection. In this paper, we conduct thorough research on smart home traffic obfuscation. We first propose a fixed-value obfuscation scheme and prove that it is perfectly secure by showing the indistinguishability of user activities. Yet, fixed-value obfuscation has high bandwidth con-sumption. To further reduce the bandwidth consumption, we propose combining fixed-value obfuscation with Multipath TCP transmission. The security and performance of the proposed mul-tipath fixed-value obfuscation method are theoretically analyzed. We have implemented the proposed methods and tested them on public packet traces and simulated smart home networks. The experimental results match well with the theoretical analysis.
Toward Secure and Private Federated Learning for IoT using Blockchain

Recent advances in the Internet of Things (IoT) offer a plethora of new opportunities for several intelligent services and applications. As the IoT connects a massive number of devices, inevitable security threats must be addressed. On the one hand, machine learning (ML), especially federated learning (FL), is proposed as a promising distributed ML paradigm to improve attack detection performance in the IoT network due to its privacy-preserving and lower latency advantages. On the other hand, blockchain is proposed as a decentralized technology to establish a secure and decentralized environment for IoT devices. However, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F$</tex> L and blockchain solutions are not well suited for the IoT context that suffers from resource limitations, such as limited communication bandwidth and scarce computing resources of IoT devices. In addition, traditional FL and blockchain solutions are unable to guarantee the reliability of data. In this paper, we present a decentralized FL framework powered by blockchain for security attack protection in IoT systems. In addition, we propose an oracle blockchain network that protects privacy and guarantees data reliability. The oracle blockchain acts as a trusted third party to verify the reliability of data and pattern formation at the network edge. Finally, we will formulate a resource allocation problem to allocate the necessary bandwidth to selected devices meticulously. The goal is to minimize communication between devices in the framework and prioritize devices with reliable behavior.
Modeling and Optimizing Resource-Constrained Instance-Based Transfer Learning

Transfer learning (TL) reduces the training overheads by transferring knowledge across domains/tasks. However, the advantages of TL come with computation and communication costs. Therefore, the decision to transfer knowledge between learners should be optimized while at the same time avoiding negative transfer (NT), i.e. when the source information does not improve but rather degrades the learning performance in the target. In this paper, we propose a new notion namely, regret of learner (RoL) as a quantitative measure for the learner's performance, computation costs and communication resources of TL. Then, we use a convex combination of the empirical source and target errors with respect to the feasibility and resource constraints to design an optimization model called OPTL that deploys a TL model in a resource-constrained environment to avoid NT. This model can be employed as a general framework for different ML methods and various communication scenarios and use cases by changing the unification parameters. To validate our approach, we use OPTL for optimized TL in a deep learning (DL)-based classification problem. Extensive experiments confirm the efficiency of our proposed method.
Segmented Learning for Class-of-Service Network Traffic Classification

Class-of-service (CoS) network traffic classification (NTC) classifies a group of similar traffic applications. The CoS classification is advantageous in resource scheduling for Internet service providers and avoids the necessity of remodelling. Our goal is to find a robust, lightweight, and fast-converging CoS classifier that uses fewer data in modelling and does not require specialized tools in feature extraction. The commonality of statistical features among the network flow segments motivates us to propose novel segmented learning that includes essential vector representation and a simple-segment method of classification. We represent the segmented traffic in the vector form using the essential vector representation (EVR). Then, the segmented traffic is modelled for classification using random forest based simple-segment method of classification (S2MC). Our solution's success relies on finding the optimal segment size and a minimum number of segments required in modelling. The solution is validated on multiple datasets for various CoS services, including virtual reality (VR). Significant findings of the research work are i) Synchronous services that require acknowledgment and request to continue communication are classified with 99 % accuracy, ii) Initial 1,000 packets in any session are good enough to model a CoS traffic for promising results, and we therefore can quickly deploy a CoS classifier, and iii) Test results remain consistent even when trained on one dataset and tested on a different dataset. In summary, our solution is the first to propose segmentation learning NTC that uses fewer features to classify most CoS traffic with an accuracy of 99 %. The implementation of our solution is available on GitHub.
Graph-based Multi-view Partial Multi-label Learning

In multi-view partial multi-label learning (MVPML) problem, each instance is described by several heterogeneous feature representations and associated with a set of candidate labels, which include both ground-truth and noisy labels. The key to learn from MVPML data lies in how to deal with multi-view data and how to select the ground-truth labels from candidate label set. In this paper, we propose a Graph-based Multi-view Partial Multi-label method, which integrates exploiting multi-view information, noisy label disambiguation and training predictor model into a whole framework. Specifically, we first exploit the consensus information across different views by learning the similarity graph of each view and fuses these similarity graphs into a unified graph. Secondly, we decompose the observed label set into a ground-truth label matrix and a noisy label matrix, where the noisy label matrix is assumed to be sparse. Then, we embed the learned unified similarity graph into the process of label disambiguation to obtain a more reliable ground-truth label matrix. Finally, the predictive model is learned by the ground-truth label matrix. Extensive experiments indicate that our proposed method can achieve superior or comparable performance against state-of-the-art methods.
Optimization of Clustering Strategy and Resource Allocation for Clustered Federated Learning

Federated learning (FL) framework enables user devices collaboratively train a global model based on their local datasets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Then, aiming at optimizing the model training performance, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two sub-problems. Specifically, a coalition formation algorithm is proposed for the device clustering sub-problem, and the sub-problem of bandwidth allocation and transmit power control is solved directly due to its convexity. Finally, simulation experiments are conducted on the MNIST dataset to validate the performance of the proposed algorithm in terms of test accuracy.
Taming Hybrid-Cloud Fast and Scalable Graph Analytics at Twitter

We have witnessed a boosted demand for graph analytics at Twitter in recent years, and graph analytics has become one of the key parts of Twitter's large-scale data analytics and machine learning for driving engagement, serving the most relevant content, and promoting healthier conversations. However, infrastructure for graph analytics has historically not been an area of investment at Twitter, resulting in a long timeline and huge engineering effort for each project to deal with graphs at the Twitter scale. How do we build a unified graph analytics user experience to fulfill modern data analytics on various graph scales spanning from thousands to hundreds of billions of vertices and edges? To bring fast and scalable graph analytics capability into production, we investigate the challenges we are facing in largescale graph analytics at Twitter and propose a unified graph analytics platform for efficient, scalable, and reliable graph analytics across on-premises and cloud to fulfill the requirements of diverse graph use cases and challenging scales. We also conduct quantitative benchmarking on Twitter's production-level graph use cases between popular graph analytics frameworks to certify our solution.
Federated Dynamic Spectrum Access through Multi-Agent Deep Reinforcement Learning

Dynamic spectrum access (DSA) has emerged as a promising solution for spectrum usage enhancement by allowing opportunistic access of secondary users to the licensed spectrum. In this paper, we introduce Fed-MADRL, a collaborative DSA technique that exploits both federated learning (FL) and multiagent deep reinforcement learning (MADRL). FL allows numerous users to collaborate on the system goal optimization without sharing their training data. By keeping all training data at the user's end, FL simultaneously enhances communication efficiency and protects data privacy. To further reduce communication costs, each user in Fed-MADRL only shares quantized data. To the best of our knowledge, Fed-MADRL is the first effort that employs FL in DSA networks with quantized communication. Simulation results show that the introduced Fed-MADRL approach beats the independent learning method and provides comparable results to the synchronous FL method, which involves significantly greater communication overheads.
Identity-based Secure and Efficient Intelligent Inference Framework for IoT-Cloud System

The convolutional neural network (CNN) inference framework has been used in device-cloud systems to deploy near-end fast-response intelligent services. However, outsourcing data from devices to remote cloud for model training incurs security concerns, and existing inference models suffer from inefficiency and underperforming. In this paper, we design a novel framework for secure and efficient CNN inference based on IoT-edge-cloud collaboration. A two-layer identity-based cryptography scheme is designed to prevent sensor data and model parameters from leakage and tampering. A seed-filter-based model is leveraged to reduce model parameters for transmission and encryption, without sacrificing inference performance. The security analysis proves that our cryptographic algorithms can defeat Man-in-the-Middle attacks. Experimental results also indicate that the proposed framework can adapt to the efficiency requirements of edge computing without any compromise on the performance of machine learning tasks.
Formalizing and Computing Propositional Quantifiers

A surprising result of Pitts (1992) says that propositional quantifiers are definable internally in intuitionistic propositional logic (IPC). The main contribution of this paper is to provide a formalization of Pitts’ result in the Coq proof assistant, and thus a verified implementation of Pitts’ construction. We in addition provide an OCaml program, extracted from the Coq formalization, which computes propositional formulas that realize intuitionistic versions of ∃ p φ and ∀ p φ from p and φ.