269 research outputs found
Distributed Learning over Networks with Graph-Attention-Based Personalization
In conventional distributed learning over a network, multiple agents
collaboratively build a common machine learning model. However, due to the
underlying non-i.i.d. data distribution among agents, the unified learning
model becomes inefficient for each agent to process its locally accessible
data. To address this problem, we propose a graph-attention-based personalized
training algorithm (GATTA) for distributed deep learning. The GATTA enables
each agent to train its local personalized model while exploiting its
correlation with neighboring nodes and utilizing their useful information for
aggregation. In particular, the personalized model in each agent is composed of
a global part and a node-specific part. By treating each agent as one node in a
graph and the node-specific parameters as its features, the benefits of the
graph attention mechanism can be inherited. Namely, instead of aggregation
based on averaging, it learns the specific weights for different neighboring
nodes without requiring prior knowledge about the graph structure or the
neighboring nodes' data distribution. Furthermore, relying on the
weight-learning procedure, we develop a communication-efficient GATTA by
skipping the transmission of information with small aggregation weights.
Additionally, we theoretically analyze the convergence properties of GATTA for
non-convex loss functions. Numerical results validate the excellent
performances of the proposed algorithms in terms of convergence and
communication cost.Comment: Accepted for publication in IEEE TSP; with supplementary details for
the derivation
Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?
With the aim of integrating over-the-air federated learning (AirFL) and
non-orthogonal multiple access (NOMA) into an on-demand universal framework,
this paper proposes a novel reconfigurable intelligent surface (RIS)-aided
hybrid network by leveraging the RIS to flexibly adjust the signal processing
order of heterogeneous data. The objective of this work is to maximize the
achievable hybrid rate by jointly optimizing the transmit power, controlling
the receive scalar, and designing the phase shifts. Since the concurrent
transmissions of all computation and communication signals are aided by the
discrete phase shifts at the RIS, the considered problem (P0) is a challenging
mixed integer programming problem. To tackle this intractable issue, we
decompose the original problem (P0) into a non-convex problem (P1) and a
combinatorial problem (P2), which are characterized by the continuous and
discrete variables, respectively. For the transceiver design problem (P1), the
power allocation subproblem is first solved by invoking the
difference-of-convex programming, and then the receive control subproblem is
addressed by using the successive convex approximation, where the closed-form
expressions of simplified cases are derived to obtain deep insights. For the
reflection design problem (P2), the relaxation-then-quantization method is
adopted to find a suboptimal solution for striking a trade-off between
complexity and performance. Afterwards, an alternating optimization algorithm
is developed to solve the non-linear and non-convex problem (P0) iteratively.
Finally, simulation results reveal that 1) the proposed RIS-aided hybrid
network can support the on-demand communication and computation efficiently, 2)
the performance gains can be improved by properly selecting the location of the
RIS, and 3) the designed algorithms are also applicable to conventional
networks with only AirFL or NOMA users
Probabilistic available transfer capability assessment in power systems with wind power integration
STAR-RIS Enabled Heterogeneous Networks: Ubiquitous NOMA Communication and Pervasive Federated Learning
This paper integrates non-orthogonal multiple access (NOMA) and over-the-air
federated learning (AirFL) into a unified framework using a simultaneous
transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The
STAR-RIS plays an important role in adjusting the decoding order of hybrid
users for efficient interference mitigation and omni-directional coverage
extension. To capture the impact of non-ideal wireless channels on AirFL, a
closed-form expression for the optimality gap (a.k.a. convergence upper bound)
between the actual loss and the optimal loss is derived. This analysis reveals
that the learning performance is significantly affected by active and passive
beamforming schemes as well as wireless noise. Furthermore, when the learning
rate diminishes as the training proceeds, the optimality gap is explicitly
characterized to converge with a linear rate. To accelerate convergence while
satisfying QoS requirements, a mixed-integer non-linear programming (MINLP)
problem is formulated by jointly designing the transmit power at users and the
configuration mode of STAR-RIS. Next, a trust region-based successive convex
approximation method and a penalty-based semidefinite relaxation approach is
proposed to handle the decoupled non-convex subproblems iteratively. An
alternating optimization algorithm is then developed to find a suboptimal
solution for the original MINLP problem. Extensive simulation results show that
i) the proposed framework can efficiently support NOMA and AirFL users via
concurrent uplink communications, ii) our algorithms can achieve a faster
convergence rate on the IID and non-IID settings as compared to baselines, and
iii) both the spectrum efficiency and learning performance can be significantly
improved with the aid of the well-tuned STAR-RIS.Comment: 16 pages, 8 figure
Over-the-Air Split Machine Learning in Wireless MIMO Networks
In split machine learning (ML), different partitions of a neural network (NN)
are executed by different computing nodes, requiring a large amount of
communication cost. To ease communication burden, over-the-air computation
(OAC) can efficiently implement all or part of the computation at the same time
of communication. Based on the proposed system, the system implementation over
wireless network is introduced and we provide the problem formulation. In
particular, we show that the inter-layer connection in a NN of any size can be
mathematically decomposed into a set of linear precoding and combining
transformations over MIMO channels. Therefore, the precoding matrix at the
transmitter and the combining matrix at the receiver of each MIMO link, as well
as the channel matrix itself, can jointly serve as a fully connected layer of
the NN. The generalization of the proposed scheme to the conventional NNs is
also introduced. Finally, we extend the proposed scheme to the widely used
convolutional neural networks and demonstrate its effectiveness under both the
static and quasi-static memory channel conditions with comprehensive
simulations. In such a split ML system, the precoding and combining matrices
are regarded as trainable parameters, while MIMO channel matrix is regarded as
unknown (implicit) parameters.Comment: 15 pages, 13 figures, journal pape
A comparative study on microstructure and properties of traditional laser cladding and high-speed laser cladding of Ni45 alloy coatings
High-speed laser cladding technology can significantly improve the efficiency of coating preparation and effectively widen the application range of laser cladding. In this study, the Ni45 powders were deposited on steel substrate by traditional low speed laser cladding and high-speed laser cladding process, respectively. The cladding efficiency, surface forming, cross-sectional microstructure, microhardness, wear and corrosion resistance properties of the traditional and high-speed laser cladded Ni45 alloy coatings were compared. It can be seen that the thickness of the high-speed laser cladding coating was much thinner than that of the traditional laser cladding coating. Compared with traditional laser cladding, high-speed laser cladding could achieve a cladding speed of 76.86 m/min and a cladding efficiency of 156.79 cm2/min. The microstructure of the two kinds of coatings shows the same growth law, but the microstructure in high-speed laser cladding was smaller and denser, and the columnar crystal interval was narrower, only about 6 μm. It is found that the cooling rate of the traditional laser cladding coating was smaller than that of the high-speed laser cladding, and as the cladding speed increased, the cooling rate became higher and higher. The cross-section microhardness of the traditional laser cladding coating was relatively uniform of 337 HV0.2, while the microhardness of high-speed laser cladding surface increased to about 543 HV0.2. In addition, the wear and corrosion resistance of high-speed laser cladded coatings were better than that of traditional laser cladded coatings. As the cladding speed increased, the wear and corrosion resistance of the cladded coatings became better
High performance porphyrin-based dye-sensitized solar cells with both iodine and cobalt redox shuttles
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