223 research outputs found
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
We envision a mobile edge computing (MEC) framework for machine learning (ML)
technologies, which leverages distributed client data and computation resources
for training high-performance ML models while preserving client privacy. Toward
this future goal, this work aims to extend Federated Learning (FL), a
decentralized learning framework that enables privacy-preserving training of
models, to work with heterogeneous clients in a practical cellular network. The
FL protocol iteratively asks random clients to download a trainable model from
a server, update it with own data, and upload the updated model to the server,
while asking the server to aggregate multiple client updates to further improve
the model. While clients in this protocol are free from disclosing own private
data, the overall training process can become inefficient when some clients are
with limited computational resources (i.e. requiring longer update time) or
under poor wireless channel conditions (longer upload time). Our new FL
protocol, which we refer to as FedCS, mitigates this problem and performs FL
efficiently while actively managing clients based on their resource conditions.
Specifically, FedCS solves a client selection problem with resource
constraints, which allows the server to aggregate as many client updates as
possible and to accelerate performance improvement in ML models. We conducted
an experimental evaluation using publicly-available large-scale image datasets
to train deep neural networks on MEC environment simulations. The experimental
results show that FedCS is able to complete its training process in a
significantly shorter time compared to the original FL protocol
-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI
In the wake of the burgeoning expansion of generative artificial intelligence
(AI) services, the computational demands inherent to these technologies
frequently necessitate cloud-powered computational offloading, particularly for
resource-constrained mobile devices. These services commonly employ prompts to
steer the generative process, and both the prompts and the resultant content,
such as text and images, may harbor privacy-sensitive or confidential
information, thereby elevating security and privacy risks. To mitigate these
concerns, we introduce -Split, a split computing framework to
facilitate computational offloading while simultaneously fortifying data
privacy against risks such as eavesdropping and unauthorized access. In
-Split, a generative model, usually a deep neural network (DNN), is
partitioned into three sub-models and distributed across the user's local
device and a cloud server: the input-side and output-side sub-models are
allocated to the local, while the intermediate, computationally-intensive
sub-model resides on the cloud server. This architecture ensures that only the
hidden layer outputs are transmitted, thereby preventing the external
transmission of privacy-sensitive raw input and output data. Given the
black-box nature of DNNs, estimating the original input or output from
intercepted hidden layer outputs poses a significant challenge for malicious
eavesdroppers. Moreover, -Split is orthogonal to traditional
encryption-based security mechanisms, offering enhanced security when deployed
in conjunction. We empirically validate the efficacy of the -Split
framework using Llama 2 and Stable Diffusion XL, representative large language
and diffusion models developed by Meta and Stability AI, respectively. Our
-Split implementation is publicly accessible at
https://github.com/nishio-laboratory/lambda_split.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
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
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
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