INFOCOM 2024 Papers

The emerging Web 3.0 paradigm aims to decentralize existing web services, enabling desirable properties such as transparency, incentives, and privacy preservation. However, current Web 3.0 applications supported by blockchain infrastructure still cannot support complex data analytics tasks in a scalable and privacy-preserving way. This paper introduces the emerging federated analytics (FA) paradigm into the realm of Web 3.0 services, enabling data to stay local while still contributing to complex web analytics tasks in a privacy-preserving way. We propose FedWeb, a tailored FA design for important frequent pattern mining tasks in Web 3.0. FedWeb remarkably reduces the number of required participating data owners to support privacy-preserving Web 3.0 data analytics based on a novel distributed differential privacy technique. The correctness of mining results is guaranteed by a theoretically rigid candidate filtering scheme based on Hoeffding’s inequality and Chebychev’s inequality. Two response budget saving solutions are proposed to further reduce participating data owners. Experiments on three representative Web 3.0 scenarios show that FedWeb can improve data utility by ∼25.3% and reduce the participating data owners by ∼98.4%.

Federated Learning (FL) enables distributed clients to collaboratively train models without exposing their private data. However, it is difficult to implement efficient FL due to limited resources. Most existing works compress the transmitted gradients or prune the global model to reduce the resource cost, but leave the compressed or pruned parameters under-optimized, which degrades the training performance. To address this issue, the neural composition technique constructs size-adjustable models by composing low-rank tensors, allowing every parameter in the global model to learn the knowledge from all clients. Nevertheless, some tensors can only be optimized by a small fraction of clients, thus the global model may get insufficient training, leading to a long completion time, especially in heterogeneous edge scenarios. To this end, we enhance the neural composition technique, enabling all parameters to be fully trained. Further, we propose a lightweight FL framework, called Heroes, with enhanced neural composition and adaptive local update. A greedy-based algorithm is designed to adaptively assign the proper tensors and local update frequencies for participating clients according to their heterogeneous capabilities and resource budgets. Extensive experiments demonstrate that Heroes can reduce traffic consumption by about 72.05% and provide up to 2.97× speedup compared to the baselines.

Software-Defined Networks (SDN) revolutionize traditional networks by separating control and data planes for enhanced agility and programmability. This separation, however, also opens up vulnerabilities, allowing adversaries to manipulate data plane forwarding and breach security policies. To counter this, we propose a Lightweight Path Validation Scheme (L-PVS) specifically designed for SDN environments. Our approach uses a simple validation scheme for packet forwarding paths that verifies the paths traversed by packets. Then, we further amplify the scheme with a network flow path validation to boost the validation efficiency. To reduce storage demands on switches during flow path validation, we develop a storage optimization method that aligns switch storage overhead with network flows rather than individual packets. Furthermore, we formulate a path partition scheme and present a Greedy-based KeySwitch Node Selection Algorithm (GKSS) to pinpoint optimal switches for path partition, significantly reducing overall data plane storage usage. Lastly, we design a technique using temporary KeySwitch nodes to identify anomaly switches when the controller encounters path validation failure. Evaluation results verify that L-PVS facilitates path validation with a reduced validation header size while minimizing the impact on processing delay and switch storage overhead.

The construction of Low Earth Orbit (LEO) satellite constellations has recently spurred tremendous attention from academia and industry. 5G and 6G standards have specified LEO satellite network as a key component of 5G and 6G networks. However, ground terminals experience frequent, high-latency handover incurred by satellites’ fast travelling speed, which deteriorates the performance of latency-sensitive applications. To address this challenge, we propose a novel handover flowchart for mobile satellite networks, which can considerably reduce the handover latency. The innovation behind this scheme is to mitigate the interaction between the access and core networks that occupy the majority of time overhead by leveraging the predictable travelling trajectory and spatial distribution inherent in mobile satellite networks. Specifically, we design a fine-grained synchronized algorithm to address the synchronization problem due to the lack of control signalling delivery between the access and core networks. Moreover, we minimize the computational complexity of the core network using information such as the satellite access strategy and unique spatial distribution, which is caused by frequent prediction operations. We have built a prototype for a mobile satellite network using modified Open5GS and UERANSIM, which is driven by actual LEO satellite constellations such as Starlink and Kuiper. We have conducted extensive experiments, and the results demonstrate that our proposed handover scheme can considerably reduce the handover latency compared to the 3GPP Non-terrestrial Networks (NTN) and two other existing handover schemes.

Backscatter is an enabling technology for battery-free sensing in industrial IoT applications. For the purpose of full coverage of numerous tags in the deployment area, one often needs to deploy multiple readers, each of which is to communicate with tags within its communication range. But the actual backscattered signals from a tag are likely to reach a reader outside its communication range, causing undesired interference. Conventional approaches for interference avoidance, either TDMA or CSMA based, separate the readers’ media accesses in the time dimension and suffer from limited network throughput. In this paper, we propose Trident, a novel backscatter tag design that enables interference avoidance with frequency-space division. By incorporating a tunable bandpass filter and multiple terminal loads, a Trident tag is able to detect its channel condition and adaptively adjust the frequency band and the power of its backscattered signals, so that all the readers in the network can operate concurrently without being interfered. We implement Trident and evaluate its performance under various settings. The results demonstrate that Trident enhances the network throughput by 3.18×, compared to the TDMA based scheme.

This paper studies a new but important pattern for items in data streams, called promising items. The promising items mean that the frequencies of an item in multiple continuous time windows show an upward trend overall, while a slight decrease in some of these windows is allowed. Many practical applications can benefit from the property of promising items, e.g., detecting potential hot events or news in social networks, preventing network congestion in communication channels, and monitoring latent attacks in computer networks. To accurately find promising items in data streams in real-time under limited memory space, we propose a novel structure named Scout Sketch, which consists of Filter and Finder. Filter is devised based on the Bloom filter to eliminate the ungratified items with less memory overload; Finder records some necessary information about the potential items and detects the promising items at the end of each time window, where we propose some tailor-made detection operations. We also analyze the theoretical performance of Scout Sketch. Finally, we conducted extensive experiments based on four real-world datasets. The experimental results show that the F1 Score and throughput of Scout Sketch are about 2.02 and 5.61 times that of the compared solutions, respectively.

DNS relies on domain delegation for good scalability, where domains delegate their resolution service to authoritative nameservers. However, such delegations lead to complex inter-dependencies between DNS zones. While a complex dependency might improve the robustness of domain resolution, it could also introduce security risks unexpectedly. In this work, we perform a large-scale measurement on nearly 217M domains to analyze their resolution dependencies at both zone level and infrastructure level. According to our analysis, domains under country-code TLDs and new generic TLDs generally present more complicated dependency relationships. For robustness consideration, popular domains prefer to configure more complex dependencies. However, the centralization of nameserver hosting and the silent outsourcing of DNS providers could lead to severe false redundancy at infrastructure level. Worse, considerable domain configurations in the wild are "not robust but risky": a more complex dependency may also indicate more vulnerabilities, e.g., domains with a 2× higher dependency complexity have a 2.87× larger proportion suffering from the hijacking risk brought by lame delegation.

Federated graph learning (FGL) enables multiple participants with distributed but connected graph data to collaboratively train a model in a privacy-preserving way. However, the high communication cost hinder the adoption of FGL in many resource-limited or delay-sensitive applications. In this work, we focus on reducing the communication cost incurred by the transmission of neighborhood information in FGL. We propose to search for local proxies that can play a substitute role as the external neighbors, and develop a novel federated graph learning framework named GraphProxy. GraphProxy utilizes representation similarity and class correlation to select local proxies for external neighbors. And we propose to dynamically adjust the proxy strategy according to the changing representation of nodes during the iterative training process. We also perform a theoretical analysis and show that using a proxy node has a similar influence on training when it is sufficiently similar to the external one. Extensive evaluations show the effectiveness of our design, e.g., GraphProxy can achieve 8× communication efficiency with only 0.14% performance degradation.

Reliable transmission of fragile quantum information requires one to efficiently select and utilize high-fidelity links among multiple noisy quantum links. However, the fidelity, a quality metric of quantum links, is unknown a priori. Uniformly estimating the fidelity of all links can be expensive, especially in networks with numerous links. To address this challenge, we formulate the link selection and fidelity estimation problem as a best arm identification problem and propose an algorithm named LinkSelFiE. The algorithm efficiently identifies the optimal link from a set of quantum links and provides an accurate fidelity estimate of that link with low quantum resource consumption. LinkSelFiE estimates link fidelity based on the feedback of a vanilla network benchmarking subroutine, and adaptively eliminates inferior links throughout the whole fidelity estimation process. This elimination leverages a novel confidence interval derived in this paper for the estimates from the subroutine, which theoretically guarantees that LinkSelFiE outputs the optimal link correctly with high confidence. We also establish a provable upper bound of cost complexity for LinkSelFiE. Moreover, we perform extensive simulations under various scenarios to corroborate that LinkSelFiE outperforms other existing methods in terms of both identifying the optimal link and reducing quantum resource consumption.

Decentralized federated learning (DFL) overcomes the single point of failure issue of centralized federated learning. Building upon DFL, blockchain-based federated learning (BFL) takes further strides in establishing trust, enhancing security, and fault tolerance. However, BFL based on the classical linear blockchain exhibits diminished training efficiency in heterogeneous environments and is limited by the performance bottleneck of blockchain. Recent solutions introduce the directed acyclic graph (DAG) blockchain to address these issues, yet they compromise the verifiability of BFL, struggle with handling outdated models, and have a slow convergence speed. In this paper, we propose TGFL, a decentralized federated learning framework based on the Tree-Graph blockchain. The underlying blockchain structure of TGFL is designed as a block-centered DAG to support verifiable and semi-asynchronous training. To facilitate fast convergence, we design a pivot chain generation algorithm that topologically sorts the semi-asynchronous training process, guiding participants in sampling appropriate models. The consensus mechanism, which is closely integrated with federated learning, ensures that the TGFL can effectively resist attacks on the model and the blockchain system. Extensive experiments in various settings demonstrate that TGFL can achieve better training efficiency and model accuracy compared to three baselines.

Remote Direct Memory Access (RDMA) has been widely adopted in modern data centers thanks to its high-throughput, low-latency data transfer capability and reduced CPU overhead. However, traditional network-flow-based monitoring is poor at interpreting RDMA communication and hence inadequate for gaining insights. In this paper, we present INSERT, an end-to-end RDMA telemetry system that enables seamless visibility into RDMA communication from the network layer all the way to the application layer. To this end, INSERT combines (i) eBPF-based transparent RDMA tracing on end-hosts and (ii) stateful RDMA network telemetry on programmable data plane. We implement RDMA network telemetry on programmable SmartNICs, where we address practical challenges for maintaining fine-grained state on massively-parallel packet processing pipelines. We demonstrate that INSERT can perform reasonably accurate telemetry at line-rate for different types of RDMA traffic even in the presence of out-of-order packets, and finally showcase two practical use cases that can benefit from it.

We address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. Real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to a reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.

Continuous blood pressure (BP) monitoring using wearable devices has received increasing attention due to its importance in diagnosing diseases. However, existing methods mainly measure BP intermittently, involve some form of user effort, and suffer from insufficient accuracy due to sensor properties. In order to overcome these limitations, we study the BP measurement technology based on heart sounds, and find that the time interval between the first and second heart sounds (TIFS) of bone-conducted heart sounds collected in the binaural canal is closely related to BP. Motivated by this, we propose HearBP, a novel BP monitoring system that utilizes inear microphones to collect bone-conducted heart sounds in the binaural canal. We first design a noise removing method based on U-net autoencoder-decoder to separate clean heart sounds from background noises. Then, we design a feature extraction method based on shannon energy and energy-entropy ratio to further mine the time domain and frequency domain features of heart sounds. In addition, combined with the principal component analysis algorithm, we achieve feature dimension reduction to extract the main features related to BP. Finally, we propose a network model based on dendritic neural regression to construct a mapping between the extracted features and BP. Extensive experiments with 41 participants show the average estimation error of 0.97mmHg and 1.61mmHg and the standard deviation error of 3.13mmHg and 3.56mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range specified by the FDA’s AAMI protocol.

Counting distinct elements (also named flow cardinality) of large data streams in the network is of primary importance since it can be used for many practical monitoring applications, including DDoS attack and malware spread detection. However, modern intrusion detection systems are struggling to reduce both memory and computational overhead for such measurements. Many algorithms are designed to estimate flow cardinality, in which HyperLogLog has been proven the most efficient due to its high accuracy and low memory usage. While HyperLogLog provides good performance on flow cardinality estimation, it has inherent algorithmic vulnerabilities that lead to both security and robustness issues. To overcome these issues, we first investigate two possible threats in HyperLogLog, and propose corresponding detection and protection solutions. Lever-aging proposed solutions, we introduce CARBINE, an approach that aims at identifying and eliminating the threats that most probably mislead the output of HyperLogLog. We implement our CARBINE to evaluate the threat detection performance, especially in case of a practical network scenario under volumetric DDoS attack. The results show that our CARBINE can effectively detect different kinds of threats while performing even higher accuracy and update speed than original HyperLogLog.

5G mobile networks leverage Network Function Virtualization (NFV) to offer services in the form of network slices. Each network slice is a logically isolated fragment constructed by service chaining a set of Virtual Network Functions (VNFs). The Network Repository Function (NRF) acts as a central OpenAuthorization (OAuth) 2.0 server to secure inter-VNF communications resulting in a single point of failure. Thus, we propose 5G-WAVE, a decentralized authorization framework for the 5G core by leveraging the WAVE framework and integrating it into the OpenAirInterface (OAI) 5G core. Our design relies on Side-Car Proxies (SCPs) deployed alongside individual VNFs, allowing point-to-point authorization. Each SCP acts as a WAVE engine to create entities and attestations and verify incoming service requests. We measure the authorization latency overhead for VNF registration, 5G Authentication and Key Agreement (AKA), and data session setup and observe that WAVE verification introduces 155ms overhead to HTTP transactions for decentralizing authorization. Additionally, we evaluate the scalability of 5G-WAVE by instantiating more network slices to observe 1.4x increase in latency with 10x growth in network size. We also discuss how 5G-WAVE can significantly reduce the 5G attack surface without using OAuth 2.0 while addressing several key issues of 5G standardization.

Combinatorial multi-armed bandit refers to the model that aims to maximize cumulative rewards in the presence of uncertainty. Motivated by two important wireless network applications, in addition to maximizing cumulative rewards, it is important to ensure fairness among arms (i.e., the minimum average reward required by each arm) and reward regularity (i.e., how often each arm receives the reward). In this paper, we develop a parameterized regular and fair learning algorithm to achieve these three objectives. In particular, the proposed algorithm linearly combines virtual queue-lengths (tracking the fairness violations), Time-Since-Last-Reward (TSLR) metrics, and Upper Confidence Bound (UCB) estimates in its weight measure. Here, TSLR is similar to age-of-information and measures the elapsed number of rounds since the last time an arm received a reward, capturing the reward regularity performance, and UCB estimates are utilized to balance the tradeoff between exploration and exploitation in online learning. Through capturing a key relationship between virtual queue-lengths and TSLR metrics and utilizing several non-trivial Lyapunov functions, we analytically characterize zero cumulative fairness violation, reward regularity, and cumulative regret performance under our proposed algorithm. These findings are corroborated by our extensive simulations.

By allowing users to erase their data’s impact on federated learning models, federated unlearning protects users’ right to be forgotten and data privacy. Despite a burgeoning body of research on federated unlearning’s technical feasibility, there is a paucity of literature investigating the considerations behind users’ requests for data revocation. This paper proposes a non-cooperative game framework to study users’ data revocation strategies in federated unlearning. We prove the existence of a Nash equilibrium. However, users’ best response strategies are coupled via model performance and unlearning costs, which makes the equilibrium computation challenging. We obtain the Nash equilibrium by establishing its equivalence with a much simpler auxiliary optimization problem. We also summarize users’ multi-dimensional attributes into a single-dimensional metric and derive the closed-form characterization of an equilibrium, when users’ unlearning costs are negligible. Moreover, we compare the cases of allowing and forbidding partial data revocation in federated unlearning. Interestingly, the results reveal that allowing partial revocation does not necessarily increase users’ data contributions or payoffs due to the game structure. Additionally, we demonstrate that positive externalities may exist between users’ data revocation decisions when users incur unlearning costs, while this is not the case when their unlearning costs are negligible.

The proliferation of real-time status updating applications and ubiquitous mobile devices have motivated the analysis and optimization of data freshness in the context of age of information. At the same time, increasing requirements on computer performance have inspired research on CPU scheduling, with a focus on reducing energy consumption. However, since prior CPU scheduling strategies have ignored data freshness, we formulate the first CPU scheduling problem that aims to minimize the long-term average age of information, subject to an average power constraint. In particular, we optimize CPU scheduling strategies that specify when the CPU sleeps and adapt the CPU speed (clock frequency) during the execution of update-processing tasks. We formulate the age-minimal CPU scheduling problem as a constrained semi-Markov decision process (SMDP) problem with uncountable space. We develop a value-iteration-based algorithm and further prove its convergence in infinite space to obtain the optimal policy. Compared with existing benchmarks in terms of long-term average AoI, numerical results show that our proposed scheme can reduce the AoI by up to 53%, and obtains greater benefits when faced with a tighter power constraint. In addition, for a given AoI target, the age-minimal CPU scheduling policy can save more than 50% on energy consumption.

Surveillance cameras are ubiquitous nowadays and users’ increasing needs for accessing real-world information (e.g., finding abandoned luggage) have urged object queries in real-time videos. While recent real-time video query processing systems exhibit excellent performance, they lack utility in deployment in practice as they overlook some crucial aspects, including multi-camera exploration, resource contention, and content awareness. Motivated by these issues, we propose a framework Gecko, to provide resource-efficient and accurate real-time object queries of massive videos on edge devices. Gecko (i) obtains optimal models from the model zoo and assigns them to edge devices for executing current queries, (ii) optimizes resource usage of the edge cluster at runtime by dynamically adjusting the frame query interval of each video stream and forking/joining running models on edge devices, and (iii) improves accuracy in changing video scenes by fine-grained stream transfer and continuous learning of models. Our evaluation with real-world video streams and queries shows that Gecko achieves up to 2x more resource efficiency gains and increases overall query accuracy by at least 12% compared with prior work, further delivering excellent scalability for practical deployment.