4,676 research outputs found
Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning
In this study, we propose a digital over-the-air computation (OAC) scheme for
achieving continuous-valued (analog) aggregation for federated edge learning
(FEEL). We show that the average of a set of real-valued parameters can be
calculated approximately by using the average of the corresponding numerals,
where the numerals are obtained based on a balanced number system. By
exploiting this key property, the proposed scheme encodes the local stochastic
gradients into a set of numerals. Next, it determines the positions of the
activated orthogonal frequency division multiplexing (OFDM) subcarriers by
using the values of the numerals. To eliminate the need for precise
sample-level time synchronization, channel estimation overhead, and channel
inversion, the proposed scheme also uses a non-coherent receiver at the edge
server (ES) and does not utilize a pre-equalization at the edge devices (EDs).
We theoretically analyze the MSE performance of the proposed scheme and the
convergence rate for a non-convex loss function. To improve the test accuracy
of FEEL with the proposed scheme, we introduce the concept of adaptive absolute
maximum (AAM). Our numerical results show that when the proposed scheme is used
with AAM for FEEL, the test accuracy can reach up to 98% for heterogeneous data
distribution.Comment: Accepted for publication in IEEE Transactions on Wireless
Communications. arXiv admin note: substantial text overlap with
arXiv:2209.1100
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
Federated edge learning is envisioned as the bedrock of enabling intelligence
in next-generation wireless networks, but the limited spectral resources often
constrain its scalability. In light of this challenge, a line of recent
research suggested integrating analog over-the-air computations into federated
edge learning systems, to exploit the superposition property of electromagnetic
waves for fast aggregation of intermediate parameters and achieve (almost)
unlimited scalability. Over-the-air computations also benefit the system in
other aspects, such as low hardware cost, reduced access latency, and enhanced
privacy protection. Despite these advantages, the interference introduced by
wireless communications also influences various aspects of the model training
process, while its importance is not well recognized yet. This article provides
a comprehensive overview of the positive and negative effects of interference
on over-the-air computation-based edge learning systems. The potential open
issues and research trends are also discussed.Comment: 7 pages, 6 figures. Accepted by IEEE Communications Magazin
CFLIT: Coexisting Federated Learning and Information Transfer
Future wireless networks are expected to support diverse mobile services,
including artificial intelligence (AI) services and ubiquitous data
transmissions. Federated learning (FL), as a revolutionary learning approach,
enables collaborative AI model training across distributed mobile edge devices.
By exploiting the superposition property of multiple-access channels,
over-the-air computation allows concurrent model uploading from massive devices
over the same radio resources, and thus significantly reduces the communication
cost of FL. In this paper, we study the coexistence of over-the-air FL and
traditional information transfer (IT) in a mobile edge network. We propose a
coexisting federated learning and information transfer (CFLIT) communication
framework, where the FL and IT devices share the wireless spectrum in an OFDM
system. Under this framework, we aim to maximize the IT data rate and guarantee
a given FL convergence performance by optimizing the long-term radio resource
allocation. A key challenge that limits the spectrum efficiency of the
coexisting system lies in the large overhead incurred by frequent communication
between the server and edge devices for FL model aggregation. To address the
challenge, we rigorously analyze the impact of the computation-to-communication
ratio on the convergence of over-the-air FL in wireless fading channels. The
analysis reveals the existence of an optimal computation-to-communication ratio
that minimizes the amount of radio resources needed for over-the-air FL to
converge to a given error tolerance. Based on the analysis, we propose a
low-complexity online algorithm to jointly optimize the radio resource
allocation for both the FL devices and IT devices. Extensive numerical
simulations verify the superior performance of the proposed design for the
coexistence of FL and IT devices in wireless cellular systems.Comment: The paper has been accepted for publication by IEEE Transactions on
Wireless Communications (March 2023
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Federated learning (FL), as an emerging edge artificial intelligence
paradigm, enables many edge devices to collaboratively train a global model
without sharing their private data. To enhance the training efficiency of FL,
various algorithms have been proposed, ranging from first-order to second-order
methods. However, these algorithms cannot be applied in scenarios where the
gradient information is not available, e.g., federated black-box attack and
federated hyperparameter tuning. To address this issue, in this paper we
propose a derivative-free federated zeroth-order optimization (FedZO) algorithm
featured by performing multiple local updates based on stochastic gradient
estimators in each communication round and enabling partial device
participation. Under non-convex settings, we derive the convergence performance
of the FedZO algorithm on non-independent and identically distributed data and
characterize the impact of the numbers of local iterates and participating edge
devices on the convergence. To enable communication-efficient FedZO over
wireless networks, we further propose an over-the-air computation (AirComp)
assisted FedZO algorithm. With an appropriate transceiver design, we show that
the convergence of AirComp-assisted FedZO can still be preserved under certain
signal-to-noise ratio conditions. Simulation results demonstrate the
effectiveness of the FedZO algorithm and validate the theoretical observations.Comment: This work was accepted to Transaction on Signal Processin
Federated AI for building AI Solutions across Multiple Agencies
The different sets of regulations existing for differ-ent agencies within the
government make the task of creating AI enabled solutions in government
dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different
agencies, which could be a significant impediment to training AI models. We
discuss the challenges that exist in environments where data cannot be freely
shared and assess tech-nologies which can be used to work around these
challenges. We present results on building AI models using the concept of
federated AI, which al-lows creation of models without moving the training data
around.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and
Public Sector, Arlington, Virginia, US
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