Robustness in Wireless Distributed Learning: An Information-Theoretic Analysis

2024-01-31

arXiv.org

AI Summary

Wireless distributed learning lacks rigorous robustness analysis under channel distortions and efficient mechanisms to enhance communication efficiency. Method: This paper pioneers an information-theoretic modeling of the inherent tolerance of learning tasks to communication impairments. By jointly characterizing the capacity–distortion trade-off and incorporating a loss-function-based divergence metric, we derive a computable upper bound on performance degradation and quantify the effective channel capacity gain attributable to the intrinsic robustness of learning. Contribution/Results: Our framework reveals the “learning-as-compression” principle, explicitly translating learning robustness into communication resource savings. Simulations demonstrate that, under task-specific accuracy constraints, the proposed approach significantly outperforms conventional decoupled communication-and-learning designs. It establishes both theoretical foundations and practical pathways for task-oriented semantic communication.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Social networks and social learning
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we take an information-theoretic approach to understand the robustness in wireless distributed learning. Upon measuring the difference in loss functions, we provide an upper bound of the performance deterioration due to imperfect wireless channels. Moreover, we characterize the transmission rate under task performance guarantees and propose the channel capacity gain resulting from the inherent robustness in wireless distributed learning. An efficient algorithm for approximating the derived upper bound is established for practical use. The effectiveness of our results is illustrated by the numerical simulations.

Problem

• Analyzing robustness in wireless distributed learning theoretically
• Deriving task-aware capacity under accuracy guarantees
• Developing robust training against channel distortions

Innovation

• Information-theoretic analysis of wireless learning robustness
• Task-aware ε-capacity for transmission rate guarantees
• Robust training framework optimizing accuracy and robustness