Communication-Efficient Edge AI: Algorithms and Systems

By Yuanming Shi, Kai Yang, Tao Jiang, Khaled Ben Letaief. IEEE Communications Surveys and Tutorials, 2020

441 citations recorded by the source corpus.

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields, ranging from speech processing, image classification to drug discovery. This is driven by the explosive growth of data, advances in machine learning (especially deep learning), and the easy access to powerful computing resources. Particularly, the wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data, which provides the opportunity to derive accurate models and develop various intelligent applications at the network edge. However, such enormous data cannot all be sent to the cloud for processing, due to the varying channel quality, traffic congestion and/or privacy concerns, and the enormous energy consumption. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative. AI at the edge requires close cooperation among edge devices, such as smart phones and smart vehicles, and edge servers at the wireless access points and base stations, which however result in heavy communication overheads. In this paper, we present a comprehensive survey of the recent developments in various techniques for overcoming these communication challenges. Specifically, we first identify key communication challenges in edge AI systems. We then introduce communication-efficient techniques, from both algorithmic and system perspectives for training and inference tasks at the network edge. Potential future research directions are also highlighted.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/47a8355e76c3675c481f135cce3a5911c74aeac3

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Citations in the corpus

Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “From the perspective of algorithms and systems, [16] csystematically summarizes the latest approaches to overcome the communication challenges caused by AI reasoning and training at the edge of the network.”
Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey (2025, neutral): “There is ongoing advancement in deploying distributed AI frameworks, such as federated learning [81] and edge AI [82], that enhance the maneuverability of Agentic AI systems in a wider operational area with dispersed installations [83].”
A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges (2022, background): “An explosive growth of data, advances in ML, and the ease of access to powerful computing resources has driven AI to achieve remarkable breakthroughs [27].”
Federated Learning on Non-IID Data: A Survey (2021, background): “Overviews of FL applications to IoT and edge devices [73, 56, 117], wireless networks [53], mobile devices [85], and healthcare [143] have also been reported.”
Federated learning for 6G communications: Challenges, methods, and future directions (2020, background): “reduce the number of rounds of model training [8].”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “With wireless connectivity, distributed learning frameworks have been overviewed in [13], [28], and [29].”
User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, background): “Still, the federated learning framework looks interesting, especially with the growing use of on-mobile artificial intelligence (AI) [260, 261].”
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, neutral): “This includes understanding how factors such as transmit power, fading, and device mobility within an edge network can affect the performance of distributed diffusion model-based AIGC computing [225].”
Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach (2021, background): “To provide an immersive user experience, DNN-based mobile applications need to be performed within the edge of wireless networks, which eliminates the excessive latency incurred by routing data to the Cloud, and is referred to as edge inference [14], [13].”
Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning (2020, neutral): “An alternative approach is the model partitioning based edge learning [7] in which the user trains only a partition of the model locally.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “edge devices are essential to reduce the uplink communication overheads, for which various data dimensionality reduction approaches have been proposed by exploiting the specific computation tasks and communication environments [29].”