In this paper, the performance optimization of federated learning (FL), when
deployed over a realistic wireless multiple-input multiple-output (MIMO)
communication system with digital modulation and over-the-air computation
(AirComp) is studied. In particular, an MIMO system is considered in which edge
devices transmit their local FL models (trained using their locally collected
data) to a parameter server (PS) using beamforming to maximize the number of
devices scheduled for transmission. The PS, acting as a central controller,
generates a global FL model using the received local FL models and broadcasts
it back to all devices. Due to the limited bandwidth in a wireless network,
AirComp is adopted to enable efficient wireless data aggregation. However,
fading of wireless channels can produce aggregate distortions in an
AirComp-based FL scheme. To tackle this challenge, we propose a modified
federated averaging (FedAvg) algorithm that combines digital modulation with
AirComp to mitigate wireless fading while ensuring the communication
efficiency. This is achieved by a joint transmit and receive beamforming
design, which is formulated as a optimization problem to dynamically adjust the
beamforming matrices based on current FL model parameters so as to minimize the
transmitting error and ensure the FL performance. To achieve this goal, we
first analytically characterize how the beamforming matrices affect the
performance of the FedAvg in different iterations. Based on this relationship,
an artificial neural network (ANN) is used to estimate the local FL models of
all devices and adjust the beamforming matrices at the PS for future model
transmission. The algorithmic advantages and improved performance of the
proposed methodologies are demonstrated through extensive numerical
experiments.

本文研究了在一个现实的无线多输入多输出（MIMO）通信系统上部署联邦学习（FL）的性能优化，研究了使用数字调制和空中计算（AirComp）的 MIMO 系统，提出了一种组合数字调制和 AirComp 的修改联邦平均（FedAvg）算法来降低无线信道的淡化和确保通信效率。

MIMO 网络中的跨层联邦学习优化

Cross-Layer Federated Learning Optimization in MIMO Networks

Departing from the classic paradigm of data-centric designs, the 6G networks
for supporting edge AI features task-oriented techniques that focus on
effective and efficient execution of AI task. Targeting end-to-end system
performance, such techniques are sophisticated as they aim to seamlessly
integrate sensing (data acquisition), communication (data transmission), and
computation (data processing). Aligned with the paradigm shift, a task-oriented
over-the-air computation (AirComp) scheme is proposed in this paper for
multi-device split-inference system. In the considered system, local feature
vectors, which are extracted from the real-time noisy sensory data on devices,
are aggregated over-the-air by exploiting the waveform superposition in a
multiuser channel. Then the aggregated features as received at a server are fed
into an inference model with the result used for decision making or control of
actuators. To design inference-oriented AirComp, the transmit precoders at edge
devices and receive beamforming at edge server are jointly optimized to rein in
the aggregation error and maximize the inference accuracy. The problem is made
tractable by measuring the inference accuracy using a surrogate metric called
discriminant gain, which measures the discernibility of two object classes in
the application of object/event classification. It is discovered that the
conventional AirComp beamforming design for minimizing the mean square error in
generic AirComp with respect to the noiseless case may not lead to the optimal
classification accuracy. The reason is due to the overlooking of the fact that
feature dimensions have different sensitivity towards aggregation errors and
are thus of different importance levels for classification. This issue is
addressed in this work via a new task-oriented AirComp scheme designed by
directly maximizing the derived discriminant gain.

本文提出了一种针对多设备分裂推理系统的任务导向的空中计算（AirComp）方案，旨在最大限度地减少聚合误差并最大化推理准确性。