114,381 research outputs found
Learning Regionally Decentralized AC Optimal Power Flows with ADMM
One potential future for the next generation of smart grids is the use of
decentralized optimization algorithms and secured communications for
coordinating renewable generation (e.g., wind/solar), dispatchable devices
(e.g., coal/gas/nuclear generations), demand response, battery & storage
facilities, and topology optimization. The Alternating Direction Method of
Multipliers (ADMM) has been widely used in the community to address such
decentralized optimization problems and, in particular, the AC Optimal Power
Flow (AC-OPF). This paper studies how machine learning may help in speeding up
the convergence of ADMM for solving AC-OPF. It proposes a novel decentralized
machine-learning approach, namely ML-ADMM, where each agent uses deep learning
to learn the consensus parameters on the coupling branches. The paper also
explores the idea of learning only from ADMM runs that exhibit high-quality
convergence properties, and proposes filtering mechanisms to select these runs.
Experimental results on test cases based on the French system demonstrate the
potential of the approach in speeding up the convergence of ADMM significantly.Comment: 11 page
Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise
We propose an improved convergence analysis technique that characterizes the
distributed learning paradigm of federated learning (FL) with imperfect/noisy
uplink and downlink communications. Such imperfect communication scenarios
arise in the practical deployment of FL in emerging communication systems and
protocols. The analysis developed in this paper demonstrates, for the first
time, that there is an asymmetry in the detrimental effects of uplink and
downlink communications in FL. In particular, the adverse effect of the
downlink noise is more severe on the convergence of FL algorithms. Using this
insight, we propose improved Signal-to-Noise (SNR) control strategies that,
discarding the negligible higher-order terms, lead to a similar convergence
rate for FL as in the case of a perfect, noise-free communication channel while
incurring significantly less power resources compared to existing solutions. In
particular, we establish that to maintain the rate of
convergence like in the case of noise-free FL, we need to scale down the uplink
and downlink noise by and respectively,
where denotes the communication round, . Our theoretical
result is further characterized by two major benefits: firstly, it does not
assume the somewhat unrealistic assumption of bounded client dissimilarity, and
secondly, it only requires smooth non-convex loss functions, a function class
better suited for modern machine learning and deep learning models. We also
perform extensive empirical analysis to verify the validity of our theoretical
findings
Testing of Hybrid Quantum-Classical K-Means for Nonlinear Noise Mitigation
Nearest-neighbour clustering is a simple yet powerful machine learning
algorithm that finds natural application in the decoding of signals in
classical optical-fibre communication systems. Quantum k-means clustering
promises a speed-up over the classical k-means algorithm; however, it has been
shown to currently not provide this speed-up for decoding optical-fibre signals
due to the embedding of classical data, which introduces inaccuracies and
slowdowns. Although still not achieving an exponential speed-up for NISQ
implementations, this work proposes the generalised inverse stereographic
projection as an improved embedding into the Bloch sphere for quantum distance
estimation in k-nearest-neighbour clustering, which allows us to get closer to
the classical performance. We also use the generalised inverse stereographic
projection to develop an analogous classical clustering algorithm and benchmark
its accuracy, runtime and convergence for decoding real-world experimental
optical-fibre communication data. This proposed `quantum-inspired' algorithm
provides an improvement in both the accuracy and convergence rate with respect
to the k-means algorithm. Hence, this work presents two main contributions.
Firstly, we propose the general inverse stereographic projection into the Bloch
sphere as a better embedding for quantum machine learning algorithms; here, we
use the problem of clustering quadrature amplitude modulated optical-fibre
signals as an example. Secondly, as a purely classical contribution inspired by
the first contribution, we propose and benchmark the use of the general inverse
stereographic projection and spherical centroid for clustering optical-fibre
signals, showing that optimizing the radius yields a consistent improvement in
accuracy and convergence rate.Comment: 2023 IEEE Global Communications Conference: Selected Areas in
Communications: Quantum Communications and Computin
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