arXiv subjects

Heekang Song

Publications and source records attributed to Heekang Song.

4 recordsLinked to original sources

arXiv2026

Communication-Efficient Approximate Gradient Coding for Distributed Learning in Heterogeneous Systems

We propose a communication-efficient optimally structured gradient coding scheme to jointly address straggler resilience and communication efficiency in heterogeneous distributed learning. By establishing a unified framework that simultaneously optimizes gradient coding and quantization, we formulate an optimization problem to minimize residual error subject to an unbiasedness constraint. We rigorously establish the joint global optimum by deriving a closed-form code structure coupled with an optimal bit allocation strategy, while simultaneously proposing a low-complexity bit allocation algorithm that efficiently yields near-optimal performance. We provide rigorous convergence analysis for convex and smooth functions. Experiments on the COCO dataset demonstrate that our joint design significantly accelerates convergence and enhances communication efficiency compared to existing baselines.

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arXiv2025

Joint Design of Embedded Index Coding and Beamforming for MIMO-based Distributed Computing via Multi-Agent Reinforcement Learning

In distributed computing systems, reducing the communication load during the data shuffling phase is a critical challenge, as excessive inter-node transmissions are a major performance bottleneck. One promising approach to alleviate this burden is Embedded Index Coding (EIC), which exploits cached data at user nodes to encode transmissions more efficiently. However, most prior work on EIC has focused on minimizing code length in wired, error-free environments-an objective often suboptimal for wireless multiple-input multiple-output (MIMO) systems, where channel conditions and spatial multiplexing gains must be considered. This paper investigates the joint design of EIC and transmit beamforming in MIMO systems to minimize total transmission time, an NP-hard problem. We first present a conventional optimization method that determines the optimal EIC via exhaustive search. To address its prohibitive complexity and adapt to dynamic wireless environments, we propose a novel, low-complexity multi-agent reinforcement learning (MARL) framework. The proposed framework enables decentralized agents to act on local observations while effectively managing the hybrid action space of discrete EIC selection and continuous beamforming design. Simulation results demonstrate that the proposed MARL-based approach achieves near-optimal performance with significantly reduced complexity, underscoring its effectiveness and practicality for real-world wireless systems.

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arXiv2025

Approximate Gradient Coding for Distributed Learning with Heterogeneous Stragglers

In this paper, we propose an optimally structured gradient coding scheme to mitigate the straggler problem in distributed learning. Conventional gradient coding methods often assume homogeneous straggler models or rely on excessive data replication, limiting performance in real-world heterogeneous systems. To address these limitations, we formulate an optimization problem minimizing residual error while ensuring unbiased gradient estimation by explicitly considering individual straggler probabilities. We derive closed-form solutions for optimal encoding and decoding coefficients via Lagrangian duality and convex optimization, and propose data allocation strategies that reduce both redundancy and computation load. We also analyze convergence behavior for $\lambda$-strongly convex and $\mu$-smooth loss functions. Numerical results show that our approach significantly reduces the impact of stragglers and accelerates convergence compared to existing methods.

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arXiv2024

Terahertz Communication Multi-UAV-Assisted Mobile Edge Computing System

Mobile edge computing (MEC) and terahertz (THz)enabled unmanned aerial vehicle (UAV) communication systems are gaining significant attention for improving user service delays in future mobile networks. This article introduces a novel multi-UAV-aided MEC system operating at THz frequencies to minimize expected user service delays, including communication and computation latency. We address this challenge by jointly optimizing UAV relay selection, power control, positioning, and user-resource association for task offloading and resource allocation. To tackle the problem's complexities, we decompose it into four subproblems, each solved optimally with our proposed algorithm. An iterative penalty dual decomposition (PDD) algorithm approximates the original problem's solution. Numerical results demonstrate that our PDD-based approach outperforms baseline algorithms in terms of expected user service delay.

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