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.

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.

Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial intelligence paradigm, where over-the-air computation enables spectral-efficient model aggregation by achieving simultaneous communication and aggregation. However, due to limited transmit power, the performance of over-the-air FL is limited by the device with the worst channel condition toward the edge server. In this paper, we leverage reconfigurable intelligent surface (RIS) to mitigate the communication bottleneck of over-the-air FL and explicitly characterize the corresponding convergence upper bound. The convergence analysis illustrates the detrimental impact of the accumulated aggregation error over all rounds and inspires us to formulate a time-average transmission distortion minimization problem by jointly optimizing the transceiver and RIS phase-shifts. To reduce the computation complexity and enhance the model aggregation accuracy, we develop a graph neural network (GNN) based learning algorithm to directly map channel coefficients to the optimized network parameters. By exploiting permutation equivalence and invariance properties of graphs, the parameter dimension of the proposed algorithm is independent of the number of edge devices, which reduces the computational complexity and improves the algorithmic scalability. Simulations show that the proposed algorithm speeds up the computation by three orders of magnitude compared to the baselines, while achieving performance superiority and algorithmic robustness.

Distributed data aggregation is a critical design aspect in future Internet-of-Things (IoT) networks. Over-the-air computation (AirComp) is capable of achieving ultra-fast data aggregation by exploiting the superposition property of wireless channel. However, the performance of AirComp, measured by the mean-squared-error (MSE), is generally restricted by the unfavorable channel conditions and relies on the availability of perfect channel state information (CSI). In this paper, we propose to use reconfigurable intelligent surface (RIS) to assist the wireless data aggregation in IoT networks via AirComp in the presence of imperfect CSI. By taking into account the constraints of the transmit power at the devices and the unit modulus of the RIS, we formulate an optimization problem to jointly optimize the transmit power of IoT devices, the beamforming vector at the access point, and the phase-shift matrix at the RIS under the expectation-based channel uncertainty model. We present an alternating optimization method to solve this nonconvex problem. In each iteration, the transmit power and the receive beamformer are updated according to Karush-Kuhn-Tucker conditions and a closed-form solution, respectively. Moreover, we also develop a difference-of-convex algorithm to tackle the nonconvex rank-one constraint in the problem of optimizing the phase-shift matrix. Simulation results illustrate the robustness of the proposed algorithm in terms of minimizing the AirComp distortion.

Layered-division-multiplexing (LDM) is a spectrum-efficient physical-layer technology that can simultaneously support multiple services with diversified quality of service (QoS) requirements. In this paper, we propose a reconfigurable intelligent surface (RIS) assisted LDM system, where the base station (BS) simultaneously transmits non-orthogonal unicast and broadcast messages to multiple users. Our goal is to minimize the transmit power of the BS, while taking into account the QoS requirements of all messages and the unit modulus constraint of the RIS. To this end, we formulate a joint phase-shift matrix optimization as well as unicast and broadcast beamformer design problem. However, the formulated problem is a non-convex bi-quadratic programming problem. After utilizing alternating optimization and matrix lifting techniques, we transform the problem into an alternating sequence of rank-constrained semidefinite programming (SDP) problems. By introducing a difference-of-convex (DC) representation for the rank-one constraints, we develop an efficient DC algorithm to solve the low-rank optimization problem. Simulation results demonstrate the performance gains of the proposed algorithm over the state-of-art methods in reducing the BS transmit power.

Intelligent reflecting surface (IRS) has recently attracted increasing research interests due to its great potential in enhancing the energy and spectrum efficiency for wireless networks. In this paper, we shall investigate the downlink transmit power optimization problem for an IRS-assisted multigroup multicast system, where the beamformers at the base station (BS) and the phase shifts at the IRS are jointly optimized. We take into account the quality-of-service (QoS) requirements of all users in each multicasting group as well as the constant-envelope reflection of all IRS elements. The formulated problem turns out to be a non-convex quadratically constrained bi-quadratic programming problem, due to the intricate coupling between the optimization variables and the non-convex constant-envelope constraints. To this end, we present the alternating optimization method with matrix lifting to decouple the optimization variables, followed by ensuring the feasibility of the rank-one constraints via introducing the difference-of-convex (DC) function representation. We develop an effective alternating algorithm to solve the joint optimization problem. Extensive simulation results show the superiority of the proposed algorithm in reducing the transmit power of multigroup multicast systems.