Fast and Communication-Efficient Algorithm for Distributed Support Vector Machine Training

Dass, Jyotikrishna; Sarin, Vivek; Mahapatra, Rabi

doi:10.1109/tpds.2018.2879950

Cited by 19 publications

(5 citation statements)

References 10 publications

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“…Recall that the expectation E t [ ] is conditional on the first t iterations. The law of iterated expectations in [47] is thereby applied here to establish the equality = (5) . Since the probability of selecting the column…”

Section: Lemmamentioning

confidence: 99%

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New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms

Chang

Wang

2022

BDCC

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In order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a large matrix can be processed in its factorized form, advantages arise in terms of computation, implementation, and data-compression. In this work, we propose two new parallel iterative algorithms as extensions of the Gauss–Seidel algorithm (GSA) to solve regression problems involving many variables. The convergence study in terms of error-bounds of the proposed iterative algorithms is also performed, and the required computation resources, namely time- and memory-complexities, are evaluated to benchmark the efficiency of the proposed new algorithms. Finally, the numerical results from both Monte Carlo simulations and real-world datasets are presented to demonstrate the striking effectiveness of our proposed new methods.

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Section: Lemmamentioning

confidence: 99%

“…In big-data analysis, matrices are utilized extensively in formulating problems with linear structure [4][5][6][7][8][9][10]. For example, matrix factorization techniques have been applied for topic modeling and text mining [11,12].…”

Section: Introductionmentioning

confidence: 99%

New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms

Chang

Wang

2022

BDCC

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“…The quadratic matrices associated with the time and energy constraints, P k and Q k , respectively, are given in (22) and (23). In Parallel: Send w to each learner k ∈ K…”

Section: Upper Bounds Using Lagrangian Relaxationmentioning

confidence: 99%

“…Although FL has been extensively studied in literature [12], [13], [14], [15], [19], [20], PL in particular or in general, MEL which comprises both, have been sparsely studied [16], [17], [18]. Some works have proposed algorithms for edge DL without specifically focusing on the resource allocation issues [22], [23]. Most works on edge FL that consider resource consumption only do so in generic terms [12], [13], [14], [15] without considering the heterogeneities, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…DL has been widely investigated over wired/nonheterogeneous computing and communication environments [8], [9], [10], [11]. Recently, researchers have turned their attention to deploying DL models for training on nodes or learners connected via the wireless edge [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]. DL over the wireless edge, or mobile edge learning (MEL) as we term it, is motivated by two distinct yet practical scenarios: federated learning (FL) and parallelized learning (PL).…”

Section: Introductionmentioning

confidence: 99%

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Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints

Mohammad

Sorour

Hefeida

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner across mutiple edge nodes or learners connected via a resource-constrained wireless edge network. The optimization is done such that the portion of the task allotted to each learner is completed within a given global delay constraint and a local maximum energy consumption limit. The time and energy consumed are related directly to the heterogeneous communication and computational capabilities of the learners; i.e. the proposed model is heterogeneity aware (HA). Because the resulting optimization is an NP-hard quadratically-constrained integer linear program (QCILP), a two-step suggest-and-improve (SAI) solution is proposed based on using the solution of the relaxed synchronous problem to obtain the solution to the asynchronous problem. The proposed HA asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) equal batch allocation scheme. Results from a system of 20 learners tested for various completion time and energy consumption constraints show that the proposed HA-Asyn method works better than the HU synchronous/asynchronous (HU-Sync/Asyn) approach and can provide gains of up-to 25% compared to the HA-Sync scheme.

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Distributed Composite Optimization for Multi-agent Systems with Asynchrony

Ran

et al. 2022

Complex Systems: Spanning Control and Computational Cybernetics: Foundations

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Fast and Communication-Efficient Algorithm for Distributed Support Vector Machine Training

Cited by 19 publications

References 10 publications

New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms

New Efficient Approach to Solve Big Data Systems Using Parallel Gauss–Seidel Algorithms

Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints

Distributed Composite Optimization for Multi-agent Systems with Asynchrony

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