To support ambient intelligence with federated edge learning (FEEL) over resource-constrained wireless networks, it is essential to jointly design and optimize the sensing, computation, and communication processes. In this paper, we propose an integrated sensing, computation, and communication (ISCC) enabled FEEL framework, where each edge device performs wireless sensing to enrich local datasets, executes local model training with accumulated local datasets, and transmits updated local gradients for global model aggregation. Via analyzing the convergence of ISCC-enabled FEEL, we explicitly characterize the impact of newly sensed dataset size in each training round on the optimality gap. Due to the coupling of the sensing, computation, and communication processes, we formulate a long-term optimality gap minimization problem involving the joint optimization of newly sensed dataset size, computation frequency, communication bandwidth, and transmit power. By leveraging Lyapunov optimization, we develop an online optimization algorithm, where, at each iteration, the optimization variables are all derived in closed-form. Moreover, we prove that the proposed algorithm achieves its asymptotic optimal performance and conduct simulations to show the superiority of the proposed ISCC-enabled FEEL.

Federated learning (FL) with progressive training is a promising privacy-preserving and communication-efficient framework for edge intelligence applications. Specifically, by partitioning the global model into multiple sub-models and dividing the FL training into multiple stages, FL with progressive training enables the gradual training of a large model, thereby significantly reducing the transmission overhead without compromising learning performance. However, implementing FL with progressive training over wireless networks is hindered by the limited radio and energy resources. To address these issues, we adopt over-the-air computation (AirComp) to support FL with progressive training over wireless networks. By balancing the tradeoff between the AirComp transmission distortion and the transition efficiency of progressive training, we formulate a mixed-integer optimization problem with energy and power constraints, which is further decomposed into several subproblems via Lyapunov optimization. Subsequently, we develop a low computational-complexity algorithm that jointly optimizes transmit power, receive beamforming, and transition indicator in an alternating manner. Simulation results demonstrate the effectiveness of our optimization algorithm in improving the learning performance of the considered FL system.

Federated learning (FL) enables multiple users to collaboratively train a shared model while protecting user privacy. In this paper, we investigate the transmission delay minimization problem for non-orthogonal multiple access (NOMA)-assisted FL. We analyze the convergence rate of heterogeneous quantized FL to demonstrate that the minimum quantization level among scheduled users is crucial in controlling the trade-off between the number of training rounds and the transmission delay of each round. Based on the convergence analysis, we formulate a delay minimization problem for NOMA-assisted FL and propose a communication-efficient heterogeneous compression NOMA scheme for FL. Subsequently, we develop a block coordinate descent (BCD)-based algorithm that jointly optimizes the sub channel allocation, power allocation, and quan-tization level for each scheduled user. Results reveal that our proposed algorithm significantly reduces the transmission delay while achieving the same learning performance compared with conventional FL algorithms.

The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and gigantic data aggregation. However, the scarce radio resources and stringent latency requirement make it challenging to meet these demands. To tackle these challenges, over-the-air computation (AirComp) emerges as a potential technology. Specifically, AirComp seamlessly integrates the communication and computation procedures through the superposition property of multiple-access channels, which yields a revolutionary multiple-access paradigm shift from “compute-after-communicate” to “compute-when-communicate”. By this means, AirComp enables spectral-efficient and low-latency wireless data aggregation by allowing multiple devices to occupy the same channel for transmission. In this paper, we aim to present the recent advancement of AirComp in terms of foundations, technologies, and applications. The mathematical form and communication design are introduced as the foundations of AirComp, and the critical issues of AirComp over different network architectures are then discussed along with the review of existing literature. The technologies employed for the analysis and optimization on AirComp are reviewed from the information theory and signal processing perspectives. Moreover, we present the existing studies that tackle the practical implementation issues in AirComp systems, and elaborate the applications of AirComp in Internet of Things and edge intelligent networks. Finally, potential research directions are highlighted to motivate the future development of AirComp.

Federated learning (FL) is recognized as a promising privacy-preserving distributed machine learning paradigm, given its potential to enable collaborative model training among distributed devices without sharing their raw data. However, supporting FL over wireless networks confronts the critical challenges of periodically executing power-hungry training tasks on energy-constrained devices and transmitting high-dimensional model updates over spectrum-limited channels. In this paper, we reap the benefits of both energy harvesting (EH) and over-the-air computation (AirComp) to alleviate the battery limitation by harvesting ambient energy to support both the training and transmission of local models, and to achieve low-latency model aggregation by concurrently transmitting local gradients via AirComp. We characterize the convergence of the proposed FL by deriving an upper bound of the expected optimality gap, revealing that the convergence depends on the accumulated errors due to partial device participation and model distortion, both of which further depend on dynamic energy levels. To accelerate the convergence, we formulate a joint AirComp transceiver design and device scheduling problem, which is then tackled by developing an efficient Lyapunov-based online optimization algorithm. Simulations demonstrate that, by appropriately scheduling devices and allocating energy across multiple communication rounds, our proposed algorithm achieves a much better learning performance than benchmarks.

Decentralized federated learning (FL) is capable of enabling efficient and robust collaborative model training with device-to-device (D2D) communications. However, most existing studies on decentralized FL employ half-duplex communication to achieve time-division model aggregation, which is inefficient in scenarios with massive geographically dispersed devices. To address this issue, we in this paper propose decentralized over-the-air FL (DOAFL) with full-duplex (FD) communication, where over-the-air computation (AirComp) and FD communication are fused together to enable parallel model exchange and aggregation, and antenna arrays are leveraged to suppress residual self-interference (SI). Specifically, we first conduct the convergence analysis for DOAFL to characterize the influence of the consensus error introduced by residual SI, channel fading, and receiver noise on the learning performance. Subsequently, we formulate a joint communication and computation (JC2) optimization problem with an objective to increase both the accuracy and time efficiency of the model training, followed by developing a JC2 design algorithm to efficiently optimize transceiver beamforming and computing frequencies. Simulation results verify the superiority of our proposed DOAFL in terms of training latency, residual SI suppression, and learning performance under low energy budgets.

For the purpose of training a machine learning model via exploiting data from multiple devices without compromising their privacy, federated learning (FL) has become a popular approach. Meanwhile, over-the-air computation (AirComp) enables concurrent model transmission to accelerate model aggregation in the context of FL. However, the performance of model aggregation is significantly hindered by adverse wireless channels. In this paper, we employ intelligent reflecting surface (IRS) to facilitate accurate model aggregation in AirComp-based FL. To ensure compatibility with existing communication standards, this paper adopts uniform quantization for both downlink model broadcast and uplink AirComp-based gradient aggregation. Furthermore, we quantitatively examine the impact of quantization errors on transmission accuracy and convergence bound. To mitigate signal distortion, we employ an alternating optimization algorithm that optimizes the beamforming vector at the base station, the transmit/receive scalars at the devices, and the phase shifts at the IRS. The simulation results provide compelling evidence for the effectiveness and robustness of our proposed method.

Dual-functional radar-communication (DFRC) attracts extensive attention recently, given its potential to integrate the sensing and communication processes for enhancing the spectrum efficiency and hardware utilization. Due to the co-channel interference, effective resource allocation is a critical issue for DFRC, which typically relies on the accurate channel estimation. However, the conventional estimate-then-optimize algorithms may not work well due to inaccurate channel estimation, high computation complexity, and inconsistent optimization goals. This paper considers a DRFC system with multiuser multiple-input-multiple-output (MIMO) communications and MIMO radar sensing, where an end-to-end learning algorithm is developed to tackle the aforementioned issues. We formulate an optimization problem to maximize the communication performance subject to the radar sensing constraints, via optimizing both the transmit and receive beamforming matrices, while considering channel estimation in the loop. To tackle this challenging problem, we exploit the universal approximation property of the neural network to develop an end-to-end learning algorithm to directly learn the mapping between the pilot signals and the beamforming matrices, and meanwhile appropriately design the loss function to account for the radar sensing constraints. Simulations show that our proposed algorithm achieves a much greater communication performance than the baseline algorithm, while guaranteeing the same sensing performance.

Communication bottleneck and statistical heterogeneity are two critical challenges of federated learning (FL) over wireless networks. To tackle both challenges, in this paper we propose an over-the-air computation (AirComp) assisted hierarchical personalized FL (HPFL) framework, where a device-edge-cloud based three-tier network architecture is adopted to simultaneously learn a global model and multiple personalized local models. We analyze the convergence of the AirComp-assisted HPFL framework and formulate an optimization problem to minimize the transmission distortion, which is an essential component of the convergence upper bound. An efficient algorithm is subsequently developed to optimize the transceiver design by leveraging successive convex approximation and Lagrangian duality. We conduct extensive simulations to demonstrate that our developed algorithm achieves a near-optimal performance and a much greater test accuracy than the baseline algorithms.

Benefiting from the high mobility and the line-of-sight communications, unmanned aerial vehicles (UAVs) and high-altitude platform (HAP) can be, respectively, designated as the edge and cloud servers to aggregate the local and edge models in hierarchical federated learning (HFL). To enable energy-efficient HFL, we manoeuvre the trajectories and control the transmit powers of UAVs over multi-cell wireless networks. Meanwhile, as the channels are reused in different cells, inter-cell interference is inevitable during the aggregation at UAVs, leading to performance degradation of HFL. To tackle these issues, an algorithm based on multi-agent twin delayed deep deterministic policy gradient (MATD3) is proposed to minimize the overall energy consumption of UAVs during the training process. The simulation results show that the proposed MATD3-based algorithm performs much better than the baseline schemes.

Over-the-air computation (AirComp) and millimeter wave (mmWave) communications have the feasibility to perform fast wireless data aggregation (WDA) by allowing simultaneous transmissions and providing abundant spectral resources, respectively. However, AirComp is limited by the link with the worst channel condition, while mmWave communications are vulnerable to the blockages. To address these issues, this paper proposes to leverage reconfigurable intelligent surface (RIS) aided AirComp for WDA in mmWave communication networks. To enhance the system performance, we formulate an optimization problem to minimize the mean-squared error (MSE) of WDA by jointly optimizing the receive beamforming vector of the access point, the transmit scalars of devices, and the phase-shift matrix of the RIS. To this end, we derive the closed-form expression of transmit scalars and then propose a Riemannian conjugate gradient algorithm, which can efficiently tackle the unit-modulus constraints with a low computational complexity. Compared to the baseline algorithms, simulation results reveal that the proposed algorithm achieves a faster convergence rate and a smaller MSE.

Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impact of downlink/uplink inter-cell interference on the learning performance. In this paper, we investigate FL over a multi-cell wireless network, where each cell performs a different FL task and over-the-air computation (AirComp) is adopted to enable fast uplink gradient aggregation. We conduct convergence analysis of AirComp-assisted FL systems, taking into account the inter-cell interference in both the downlink and uplink model/gradient transmissions, which reveals that the distorted model/gradient exchanges induce a gap to hinder the convergence of FL. We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells. To tackle the coupling between the downlink and uplink transmissions as well as the coupling among multiple cells, we propose a cooperative multi-cell FL optimization framework to achieve efficient interference management for downlink and uplink transmission design. Results demonstrate that our proposed algorithm achieves much better average learning performance over multiple cells than non-cooperative baseline schemes.

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple-access channels. However, the model aggregation performance is severely limited by the unfavorable wireless propagation channels. In this paper, we propose to leverage intelligent reflecting surface (IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To optimize the learning performance, we present the convergence analysis of our proposed IRS-assisted AirComp-based FL system, based on which we propose to maximize the number of scheduled devices of each communication round under certain mean-squared error (MSE) requirements. To tackle the formulated highly-intractable problem, we propose a two-step optimization framework. Specifically, we induce the sparsity of device selection in the first step, followed by solving a series of MSE minimization problems to find the maximum feasible device set in the second step. We then propose an alternating optimization framework, supported by the difference-of-convex programming for low-rank optimization, to efficiently design the aggregation beamformers at the BS and phase shifts at the IRS. Simulation results demonstrate that our proposed algorithm and the deployment of an IRS can achieve a higher FL prediction accuracy than the baseline schemes.

Fast wireless data aggregation and efficient battery recharging are two critical design challenges of Internet-of-Things (IoT) networks. Over-the-air computation (AirComp) and energy beamforming (EB) turn out to be two promising techniques that can address these two challenges, necessitating the design of wireless-powered AirComp. However, due to severe channel propagation, the energy harvested by IoT devices may not be sufficient to support AirComp. In this article, we propose to leverage the intelligent reflecting surface (IRS) that is capable of dynamically reconfiguring the propagation environment to drastically enhance the efficiency of both downlink EB and uplink AirComp in IoT networks. Due to the coupled problems of downlink EB and uplink AirComp, we further propose the joint design of energy and aggregation beamformers at the access point, downlink/uplink phase-shift matrices at the IRS, and transmit power at the IoT devices, to minimize the mean-squared error (MSE), which quantifies the AirComp distortion. However, the formulated problem is a highly intractable nonconvex quadratic programming problem. To solve this problem, we first obtain the closed-form expressions of the energy beamformer and the device transmit power, and then develop an alternating optimization framework based on difference-of-convex programming to design the aggregation beamformers and IRS phase-shift matrices. Simulation results demonstrate the performance gains of the proposed algorithm over the baseline methods and show that deploying an IRS can significantly reduce the MSE of AirComp.

Fast wireless data aggregation and efficient battery recharging are two critical design challenges of Internet of Things (IoT) networks. Over-the-air computation (AirComp) and energy beamforming (EB) are two promising techniques that can tackle these two challenges. In this paper, we propose to leverage the intelligent reflecting surface (IRS) to drastically enhance the efficiency of both downlink EB and uplink AirComp in IoT networks by exploiting the passive beamforming gains at the IRS. Due to the coupled downlink EB and uplink AirComp, we propose the joint design of energy and aggregation beamformers at the access point, downlink/uplink phase-shift matrices at the IRS, and transmit power at the IoT devices to minimize the mean-squared-error (MSE), which quantifies the AirComp distortion. However, the formulated problem is a highly intractable nonconvex quadratic programming problem. To this end, we first obtain the closed-form expressions of the energy beamformer and the transmit power, and then propose an efficient algorithm that alternatively updates other variables using semidefinite relaxation (SDR) to solve the problem. Simulation results demonstrate the performance gains of the proposed algorithm over the baseline methods and show that deploying an IRS can significantly reduce the MSE of AirComp.