349 research outputs found
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks
Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study
Group) was established to initiate discussions on new IEEE 802.11 features.
Coordinated control methods of the access points (APs) in the wireless local
area networks (WLANs) are discussed in EHT Study Group. The present study
proposes a deep reinforcement learning-based channel allocation scheme using
graph convolutional networks (GCNs). As a deep reinforcement learning method,
we use a well-known method double deep Q-network. In densely deployed WLANs,
the number of the available topologies of APs is extremely high, and thus we
extract the features of the topological structures based on GCNs. We apply GCNs
to a contention graph where APs within their carrier sensing ranges are
connected to extract the features of carrier sensing relationships.
Additionally, to improve the learning speed especially in an early stage of
learning, we employ a game theory-based method to collect the training data
independently of the neural network model. The simulation results indicate that
the proposed method can appropriately control the channels when compared to
extant methods
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule
classification focusing on (i) usefulness of gradient tree boosting (XGBoost)
and (ii) effectiveness of parameter optimization using Bayesian optimization
(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung
cancers and 37 benign lung nodules) were included from public databases of CT
images. A variant of local binary pattern was used for calculating feature
vectors. Support vector machine (SVM) or XGBoost was trained using the feature
vectors and their labels. TPE or random search was used for parameter
optimization of SVM and XGBoost. Leave-one-out cross-validation was used for
optimizing and evaluating the performance of our CADx system. Performance was
evaluated using area under the curve (AUC) of receiver operating characteristic
analysis. AUC was calculated 10 times, and its average was obtained. The best
averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were
obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters
for achieving high AUC were obtained with fewer numbers of trials when using
TPE, compared with random search. In conclusion, XGBoost was better than SVM
for classifying lung nodules. TPE was more efficient than random search for
parameter optimization.Comment: 29 pages, 4 figure
Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise
Over-the-air computation (AirComp)-based federated learning (FL) enables
low-latency uploads and the aggregation of machine learning models by
exploiting simultaneous co-channel transmission and the resultant waveform
superposition. This study aims at realizing secure AirComp-based FL against
various privacy attacks where malicious central servers infer clients' private
data from aggregated global models. To this end, a differentially private
AirComp-based FL is designed in this study, where the key idea is to harness
receiver noise perturbation injected to aggregated global models inherently,
thereby preventing the inference of clients' private data. However, the
variance of the inherent receiver noise is often uncontrollable, which renders
the process of injecting an appropriate noise perturbation to achieve a desired
privacy level quite challenging. Hence, this study designs transmit power
control across clients, wherein the received signal level is adjusted
intentionally to control the noise perturbation levels effectively, thereby
achieving the desired privacy level. It is observed that a higher privacy level
requires lower transmit power, which indicates the tradeoff between the privacy
level and signal-to-noise ratio (SNR). To understand this tradeoff more fully,
the closed-form expressions of SNR (with respect to the privacy level) are
derived, and the tradeoff is analytically demonstrated. The analytical results
also demonstrate that among the configurable parameters, the number of
participating clients is a key parameter that enhances the received SNR under
the aforementioned tradeoff. The analytical results are validated through
numerical evaluations.Comment: 6 pages, 4 figure
Decentralized and Model-Free Federated Learning: Consensus-Based Distillation in Function Space
This paper proposes a fully decentralized federated learning (FL) scheme for
Internet of Everything (IoE) devices that are connected via multi-hop networks.
Because FL algorithms hardly converge the parameters of machine learning (ML)
models, this paper focuses on the convergence of ML models in function spaces.
Considering that the representative loss functions of ML tasks e.g, mean
squared error (MSE) and Kullback-Leibler (KL) divergence, are convex
functionals, algorithms that directly update functions in function spaces could
converge to the optimal solution. The key concept of this paper is to tailor a
consensus-based optimization algorithm to work in the function space and
achieve the global optimum in a distributed manner. This paper first analyzes
the convergence of the proposed algorithm in a function space, which is
referred to as a meta-algorithm, and shows that the spectral graph theory can
be applied to the function space in a manner similar to that of numerical
vectors. Then, consensus-based multi-hop federated distillation (CMFD) is
developed for a neural network (NN) to implement the meta-algorithm. CMFD
leverages knowledge distillation to realize function aggregation among adjacent
devices without parameter averaging. An advantage of CMFD is that it works even
with different NN models among the distributed learners. Although CMFD does not
perfectly reflect the behavior of the meta-algorithm, the discussion of the
meta-algorithm's convergence property promotes an intuitive understanding of
CMFD, and simulation evaluations show that NN models converge using CMFD for
several tasks. The simulation results also show that CMFD achieves higher
accuracy than parameter aggregation for weakly connected networks, and CMFD is
more stable than parameter aggregation methods.Comment: submitted to IEEE TSIP
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