7 research outputs found
Accelerating Distributed Optimization via Over-the-Air Computing
Distributed optimization is ubiquitous in emerging applications, such as
robust sensor network control, smart grid management, machine learning,
resource slicing, and localization. However, the extensive data exchange among
local and central nodes may cause a severe communication bottleneck. To
overcome this challenge, over-the-air computing (AirComp) is a promising medium
access technology, which exploits the superposition property of the wireless
multiple access channel (MAC) and offers significant bandwidth savings. In this
work, we propose an AirComp framework for general distributed convex
optimization problems. Specifically, a distributed primaldual (DPD) subgradient
method is utilized for the optimization procedure. Under general assumptions,
we prove that DPDAirComp can asymptotically achieve zero expected constraint
violation. Therefore, DPD-AirComp ensures the feasibility of the original
problem, despite the presence of channel fading and additive noise. Moreover,
with proper power control of the users' signals, the expected non-zero
optimality gap can also be mitigated. Two practical applications of the
proposed framework are presented, namely, smart grid management and wireless
resource allocation. Finally, numerical results reconfirm DPDAirComp's
excellent performance, while it is also shown that DPD-AirComp converges an
order of magnitude faster compared to a digital orthogonal multiple access
scheme, specifically, time division multiple access (TDMA)
Non-Coherent Over-the-Air Decentralized Stochastic Gradient Descent
This paper proposes a Decentralized Stochastic Gradient Descent (DSGD)
algorithm to solve distributed machine-learning tasks over wirelessly-connected
systems, without the coordination of a base station. It combines local
stochastic gradient descent steps with a Non-Coherent Over-The-Air (NCOTA)
consensus scheme at the receivers, that enables concurrent transmissions by
leveraging the waveform superposition properties of the wireless channels. With
NCOTA, local optimization signals are mapped to a mixture of orthogonal
preamble sequences and transmitted concurrently over the wireless channel under
half-duplex constraints. Consensus is estimated by non-coherently combining the
received signals with the preamble sequences and mitigating the impact of noise
and fading via a consensus stepsize. NCOTA-DSGD operates without channel state
information (typically used in over-the-air computation schemes for channel
inversion) and leverages the channel pathloss to mix signals, without explicit
knowledge of the mixing weights (typically known in consensus-based
optimization). It is shown that, with a suitable tuning of decreasing consensus
and learning stepsizes, the error (measured as Euclidean distance) between the
local and globally optimum models vanishes with rate
after iterations. NCOTA-DSGD is evaluated numerically by solving an image
classification task on the MNIST dataset, cast as a regularized cross-entropy
loss minimization. Numerical results depict faster convergence vis-\`a-vis
running time than implementations of the classical DSGD algorithm over digital
and analog orthogonal channels, when the number of learning devices is large,
under stringent delay constraints.Comment: Submitted to the IEEE Transactions on Signal Processin
Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G
Pushing artificial intelligence (AI) from central cloud to network edge has
reached board consensus in both industry and academia for materializing the
vision of artificial intelligence of things (AIoT) in the sixth-generation (6G)
era. This gives rise to an emerging research area known as edge intelligence,
which concerns the distillation of human-like intelligence from the huge amount
of data scattered at wireless network edge. In general, realizing edge
intelligence corresponds to the process of sensing, communication, and
computation, which are coupled ingredients for data generation, exchanging, and
processing, respectively. However, conventional wireless networks design the
sensing, communication, and computation separately in a task-agnostic manner,
which encounters difficulties in accommodating the stringent demands of
ultra-low latency, ultra-high reliability, and high capacity in emerging AI
applications such as auto-driving. This thus prompts a new design paradigm of
seamless integrated sensing, communication, and computation (ISCC) in a
task-oriented manner, which comprehensively accounts for the use of the data in
the downstream AI applications. In view of its growing interest, this article
provides a timely overview of ISCC for edge intelligence by introducing its
basic concept, design challenges, and enabling techniques, surveying the
state-of-the-art development, and shedding light on the road ahead
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis
The proliferation of Internet-of-Things (IoT) devices and cloud-computing
applications over siloed data centers is motivating renewed interest in the
collaborative training of a shared model by multiple individual clients via
federated learning (FL). To improve the communication efficiency of FL
implementations in wireless systems, recent works have proposed compression and
dimension reduction mechanisms, along with digital and analog transmission
schemes that account for channel noise, fading, and interference. The prior art
has mainly focused on star topologies consisting of distributed clients and a
central server. In contrast, this paper studies FL over wireless
device-to-device (D2D) networks by providing theoretical insights into the
performance of digital and analog implementations of decentralized stochastic
gradient descent (DSGD). First, we introduce generic digital and analog
wireless implementations of communication-efficient DSGD algorithms, leveraging
random linear coding (RLC) for compression and over-the-air computation
(AirComp) for simultaneous analog transmissions. Next, under the assumptions of
convexity and connectivity, we provide convergence bounds for both
implementations. The results demonstrate the dependence of the optimality gap
on the connectivity and on the signal-to-noise ratio (SNR) levels in the
network. The analysis is corroborated by experiments on an image-classification
task.Comment: 46 pages, 9 figures, to appear in IEEE J. Sel. Areas Commu
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit
sequences. Such an approach provides efficient engineering designs that are
agnostic to the meanings of the messages or to the goal that the message
exchange aims to achieve. Next generation systems, however, can be potentially
enriched by folding message semantics and goals of communication into their
design. Further, these systems can be made cognizant of the context in which
communication exchange takes place, providing avenues for novel design
insights. This tutorial summarizes the efforts to date, starting from its early
adaptations, semantic-aware and task-oriented communications, covering the
foundations, algorithms and potential implementations. The focus is on
approaches that utilize information theory to provide the foundations, as well
as the significant role of learning in semantics and task-aware communications.Comment: 28 pages, 14 figure
Decentralized SGD with Over-the-Air Computation
We consider multiple devices with local datasets collaboratively learning a global model through device-to-device (D2D) communications. The conventional decentralized stochastic gradient descent (DSGD) solution for this problem assumes error-free orthogonal links among the devices. This is based on the assumption of an underlying communication protocol that takes care of the noise, fading, and interference in the wireless medium. In this work, we show the suboptimality of this approach by designing the communication and learning protocols jointly. We first consider a point-to-point (P2P) communication scheme by scheduling D2D transmissions in an orthogonal fashion to minimize interference. Then, we propose a novel over-the-air consensus scheme by exploiting the signal superposition property of wireless transmission, rather than avoiding interference. In the proposed OAC-MAC scheme, multiple nodes align their transmissions toward a single receiver node. For both schemes, we cast the scheduling problem as a graph coloring problem. We then numerically compare the two approaches for the distributed MNIST image classification task under various network conditions. We show that the OAC-MAC scheme attains better convergence speed and final accuracy thanks to the improved robustness against channel fading and noise. We also introduce a noise-aware version of the OAC-MAC scheme with further improvements in the convergence speed and accuracy