With explosive increase of data at the mobile edge, federated learning (FL) emerges as a promising technique to reduce data transmission costs and privacy leakage risks. Nevertheless, the huge communication overhead for an increasing volume of edge devices still restricts the FL performance. Over-the-air computation (AirComp) is viable for alleviating the communication burden in FL systems. However, there consequently appears a straggler issue restraining the performance of the over-the-air FL (OA-FL) framework, which is even worse especially when devices training a machine learning model are distributed over a relatively large service area. In this paper, we propose an unmanned aerial vehicle (UAV) enabled OA-FL scheme, where the UAV acts as a parameter server (PS) to aggregate the local gradients hierarchically for global model updating. The global aggregation frequency is tunable in the hierarchical aggregation approach, enabling it to balance the resource consumption between communication and learning. Building on this approach, we carry out a gradient-correlation-aware FL performance analysis and jointly optimize the trajectory of UAV-PS, the device selection state, and the aggregation coefficients. An algorithm based on alternating optimization (AO) is developed to solve the formulated problem, where successive convex approximation (SCA) and fractional programming (FP) are utilized for the convexification of the non-convex problem. Numerical simulation results demonstrate the effectiveness of our UAV enabled hierarchical aggregation scheme compared with several existing baselines.

The communication bottleneck of over-the-air federated learning (OA-FL) lies in aggregating the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradient aggregation by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model. We derive an upper bound on the learning performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively characterizing the gradient aggregation error. Based on the analysis results, we show that the optimal number of multiplexed data streams to minimize the upper bound on the FL learning performance loss is given by the minimum of the numbers of transmit and receive antennas. We then formulate an optimization problem for the design of precoding and post-processing matrices to minimize the gradient aggregation error. To solve this problem, we develop an efficient algorithm based on alternating optimization (AO) and Karush-Kuhn-Tucker (KKT) conditions, which effectively mitigates the impact of the gradient aggregation error. Numerical results demonstrate the superb performance of the proposed SCoM approach.

The communication bottleneck of over-the-air federated learning (OA-FL) lies in uploading the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradients uploading by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model. We derive an upper bound on the learning performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively characterizing the gradient aggregation error. We show that the optimal number of multiplexed data streams to minimize the upper bound is given by the minimum of the numbers of transmit and receive antennas. We then formulate an optimization problem of designing precoding and post-processing matrices to minimize the gradient aggregation error, and develop an efficient algorithm to solve the problem. The numerical results indicate the effectiveness of the proposed SCoM approach.

With the explosive growth of data and wireless devices, federated learning (FL) over wireless medium has emerged as a promising technology for large-scale distributed intelligent systems. Yet, the urgent demand for ubiquitous intelligence will generate a large number of concurrent FL tasks, which may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in over-the-air computation inevitably brings about the problem of inter-task interference, which poses a new challenge that needs to be appropriately addressed. In this paper, we study over-the-air federated multi-task learning (OA-FMTL) over the multiple-input multiple-output (MIMO) multiple access (MAC) channel. We propose a novel model aggregation method for the alignment of local gradients of different devices, which alleviates the straggler problem in over-the-air computation due to the channel heterogeneity. We establish a communication-learning analysis framework for the proposed OA-FMTL scheme by considering the spatial correlation between devices, and formulate an optimization problem for the design of transceiver beamforming and device selection. To solve this problem, we develop an algorithm by using alternating optimization (AO) and fractional programming (FP), which effectively mitigates the impact of inter-task interference on the FL learning performance. We show that due to the use of the new model aggregation method, device selection is no longer essential, thereby avoiding the heavy computational burden involved in selecting active devices. Numerical results demonstrate the validity of the analysis and the superb performance of the proposed scheme.

With huge amounts of data explosively increasing on the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and privacy leak risks. However, when devices in a relatively large area cooperatively train a machine learning model, the attendant straggler issue will significantly reduce the learning performance. In this paper, we propose an unmanned aerial vehicle (UAV) assisted OA-FL system, where the UAV acts as a parameter server (PS) to aggregate the local gradients hierarchically for global model updating. Under this UAV-assisted hierarchical aggregation scheme, we carry out a gradient-correlation-aware FL performance analysis. We then formulate a mean squared error (MSE) minimization problem to tune the UAV trajectory and the global aggregation coefficients based on the analysis results. An algorithm based on alternating optimization (AO) and successive convex approximation (SCA) is developed to solve the formulated problem. Simulation results demonstrate the great potential of our UAV-assisted hierarchical aggregation scheme.

Although Federated learning (FL) over wireless medium is a promising technology, a large number of concurrent FL tasks, generated by the urgent demand for ubiquitous intelligence, may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in AirComp inevitably brings about the problem of inter-task interference, which poses a new challenge. In this paper, we study over-the-air multi-task FL (OA-MTFL) over the multiple-input multiple-output (MIMO) interference channel. We establish a communication-learning analytical framework for the proposed OA-MTFL scheme by considering the spatial correlation between devices, formulate an optimization problem of designing transceiver beamforming and device selection, and develop an efficient algorithm to solve it. The numerical results demonstrate the outstanding performance of the proposed scheme.