As the tenth part of the third-generation AVS standard series for real-time speech coding, AVS3P10 is the recent standard completed in the Audio Video Coding Standards Workgroup of China (AVS). Combining the state-of-the-art deep generative networks and signal processing methods, AVS3P10 targets defining new generation neural speech codecs with high quality at low bitrates, enabling excellent experiences even when the bitrate is at 5.9 kbps with excellent error resilience. Moreover, it provides wideband and super wideband coding modes, and it supports the extension of stereo coding. Both subjective listening test and objective measurement prove the merit of AVS3P10. Especially, a lightweight model with only 880k parameters is incorporated to maintain the practicality of AVS3P10 in computational efficiency. Conclusively, AVS3P10 demonstrates the maturity of neural speech coding with broad application perspectives in real-time communication.

Although edge intelligence (EI) propels the development of Internet of Things (IoT) applications to a new stage, the distributed nature of the end-users in EI networks greatly hinders its practical deployment. First, the resources of distributed end devices are limited, including computing and transmission resources, while the intelligent model typically necessitates intensive computation and substantial data from the network end. Secondly, the resources of end devices also exhibit heterogeneity, further complicating the learning in EI. Specifically, each device varies in computational capabilities, making it challenging to synchronise updates in collaborative learning approaches. Additionally, owing to the dispersed locations, each device encounters diverse wireless conditions, impeding effective communication with the edge server. Therefore, addressing the communication and computation constraints is necessary to foster practical EI applications. While novel distributed learning (DL) algorithms and machine learning (ML)-related techniques exhibit great potential, related review work lacks. Motivated by the literature gap, we provide a comprehensive review of the latest research endeavours on facilitating efficient EI deployment via examining novel DL algorithms and ML-related techniques. We also demonstrate the interplay of computation and communication efficiency in the resource-constrained EI landscape.

Although federated learning has achieved many breakthroughs recently, the heterogeneous nature of the learning environment greatly limits its performance and hinders its real-world applications. The heterogeneous data, time-varying wireless conditions and computing-limited devices are three main challenges, which often result in an unstable training process and degraded accuracy. Herein, we propose strategies to address these challenges. Targeting the heterogeneous data distribution, we propose a novel adaptive mixing aggregation (AMA) scheme that mixes the model updates from previous rounds with current rounds to avoid large model shifts and thus, maintain training stability. We further propose a novel staleness-based weighting scheme for the asynchronous model updates caused by the dynamic wireless environment. Lastly, we propose a novel CPU-friendly computation-reduction scheme based on transfer learning by sharing the feature extractor (FES) and letting the computing-limited devices update only the classifier. The simulation results show that the proposed framework outperforms existing state-of-the-art solutions and increases the test accuracy, and training stability by up to 2.38%, 93.10% respectively. Additionally, the proposed framework can tolerate communication delay of up to 15 rounds under a moderate delay environment without significant accuracy degradation.

In this paper, we propose an opportunistic scheme for the transmission of model updates from Federated Learning (FL) clients to the server, where clients are wireless mobile users. This proposal aims to opportunistically take advantage of the proximity of users to the base station or the general condition of the wireless transmission channel, rather than traditional synchronous transmission. In this scheme, during the training, intermediate model parameters are uploaded to the server, opportunistically and based on the wireless channel condition. Then, the proactively-transmitted model updates are used for the global aggregation if the final local model updates are delayed. We apply this novel model transmission scheme to one of our previous work, which is a hybrid split and federated learning (HSFL) framework for UAVs. Simulation results confirm the superiority of using proactive transmission over the conventional asynchronous aggregation scheme for the staled model by obtaining higher accuracy and more stable training performance. Test accuracy increases by up to 13.47% with just one round of extra transmission.

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for interested tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bidding-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted. In this paper, we design novel privacy-preserving incentive mechanisms to protect users’ true bid information against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated bids to the platform. Two solutions are proposed for the platform to solve the winner selection problem with the obfuscated information, which is proved to be NP-hard. Moreover, we further propose a novel task-bid pair protection truthful incentive mechanism to further prevent privacy leakage from the set of interested tasks, where each user encrypts his interested tasks via homomorphic encryption locally, and an encrypted task clustering method is proposed to group users with the same interested tasks into the same cluster for winner selection with users’ encrypted task-bid pairs. Both of theoretical analysis and extensive experiments demonstrate the effectiveness of proposed mechanisms against the untrusted platform.

Mobile Edge Computing (MEC) is a new paradigm where mobile users can offload computation tasks to the nearby MEC server to reduce their resource consumption. Some works have pointed out that the true amount of offloaded tasks may reveal the sensitive information (e.g., device usage pattern and location information) of users, and proposed several privacy-preserving offloading mechanisms. However, to the best of our knowledge, none of them can provide strict and provable privacy guarantee. In this paper, we focus on the privacy leakage issue in computation offloading in MEC with a honest-but-curious server, and propose a novel online privacy-preserving computation offloading mechanism, called OffloadingGuard, to generate efficient offloading strategies for users in real time, which provide strict user privacy guarantee while minimizing the total cost of task computation. To this end, we design a deep reinforcement learning-based offloading model which allows each user to adaptively determine the satisfactory perturbed offloading ratio according to the time-varying channel state at each time slot to achieve trade-off between user privacy and computation cost. In particular, to strictly protect the true amount of offloaded tasks and prevent the untrusted MEC server from revealing mobile users’ privacy, a range-constrained Laplace distribution is designed to obfuscate the original offloading ratio of each user and restrict the perturbed offloading ratio in a rational range. OffloadingGuard is proved to satisfy ϵ-differential privacy, and extensive experiments demonstrate its effectiveness.

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bid-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted (e.g., honest-but-curious). In this paper, we focus on the bid protection problem in mobile crowdsensing with an untrusted platform, and propose a novel privacy-preserving incentive mechanism to protect users' true bids against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated task-bid pairs to the platform. The winner selection problem with the obfuscated task-bid pairs is formulated as an integer linear programming problem and proved to be NP-hard. We consider the optimization problem at two different scenarios, and propose a solution based on Hungarian method for single measurement and a greedy solution for multiple measurements, respectively. The proposed incentive mechanism is proved to satisfy ε-differential privacy, individual rationality and γ-truthfulness. The extensive experiments on a real-world data set demonstrate the effectiveness of the proposed mechanism against the untrusted platform.