Communication-Efficient Edge AI: Algorithms and Systems (2002.09668v1)

Abstract: 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 easy access to vastly 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 from end devices to the cloud for processing, due to the varying channel quality, traffic congestion and/or privacy concerns. 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.

• The paper presents novel strategies to reduce communication overhead in Edge AI by leveraging distributed algorithmic approaches.
• It evaluates methods like zeroth-, first-, and second-order techniques, as well as federated optimization to balance accuracy with resource constraints.
• The study outlines system design innovations, including data/model partitioning and computation offloading, to support scalable, secure Edge AI deployments.

Communication-Efficient Edge AI: Algorithms and Systems

Introduction

The proliferation of edge devices and the exponential growth of data generated at the edge have spurred significant interest in Edge AI, a paradigm that enables AI model inference and training at the network edge. This paper addresses the communication challenges inherent in deploying AI applications on edge devices, where connectivity, bandwidth, and latency are constraints. By focusing on communication-efficient algorithms and systems, the paper provides a comprehensive overview of solutions that enable data processing close to its source, thus reducing the latency and bandwidth requirements typical of centralized cloud processing.

Communication Challenges in Edge AI

Edge AI presents unique challenges compared to centralized cloud computing, primarily due to limited resources on edge devices, heterogeneous resource availability, and privacy constraints. Communication between distributed nodes often becomes the bottleneck in such systems. This bottleneck is exacerbated by the need to aggregate data and model parameters without compromising the efficiency and accuracy of AI models.

Key Challenges:

• Resource Constraints: Edge devices have limited computation, storage, and power capacities.
• Heterogeneous Environments: Variability in device capabilities and network conditions necessitates adaptive solutions.
• Privacy Concerns: Sensitive user data at the edge require secure, often federated learning approaches that minimize raw data transmission.

Communication-Efficient Algorithms

The paper surveys various algorithmic strategies to mitigate communication overhead while maintaining model accuracy and training efficiency.

1. Zeroth-Order Methods: Suitable for scenarios where only function evaluations are possible, these methods replace gradient calculations with finite-difference approximations. They are effective in distributed systems where communication needs to be minimized.
2. First-Order Methods: Incorporating stochastic gradient descent (SGD) and its variants, these methods remain at the forefront due to their simplicity and effectiveness. Techniques such as gradient quantization, sparsification, and reuse help reduce the communication load per iteration.
3. Second-Order Methods: By leveraging curvature information via approximations to the Hessian matrix, these methods can converge faster than first-order algorithms. The challenge lies in balancing the computational and communication trade-offs to achieve efficiency on resource-limited devices.
4. Federated Optimization: Focused on minimizing communication rounds, federated learning involves performing more computing locally and periodically aggregating model updates. Techniques such as federated averaging and model compression are employed to reduce data exchange requirements.

Communication-Efficient Systems

Designing system architectures that effectively reduce communication overhead is crucial for deploying Edge AI.

• Data Partition-Based Systems: These systems partition data across devices and employ federated learning to aggregate model updates. Over-the-air computation is a promising technique to compute aggregation functions directly over the communication channel, reducing communication overhead.
• Model Partition-Based Systems: By splitting models across different nodes, these systems can leverage parallel processing capabilities while maintaining model accuracy. This approach is particularly valuable for large models that do not fit into a single device's memory.
• Computation Offloading: In scenarios where local processing is infeasible, computation offloading allows device-edge synergy. Partial data processing on edge devices followed by offloading to more powerful edge servers or cloud infrastructures enhances computational efficiency and reduces latency.

Future Directions

The development of communication-efficient Edge AI is an evolving area with several promising research directions:

• Advanced Coding Techniques: Applying coding theory to reduce communication overhead and mitigate stragglers is a growing field that could enhance distributed learning environments significantly.
• Edge AI Hardware and Infrastructure: Continued advancements in dedicated AI hardware, such as TPUs and FPGAs optimized for edge applications, will support efficient model inference and training at the edge.
• Security and Privacy: As edge devices process increasingly sensitive data, ensuring secure and privacy-preserving computation remains paramount. Enhancements in secure multiparty computation and homomorphic encryption will be critical.

Conclusion

Communication efficiency is the linchpin for successful Edge AI deployment. By integrating algorithmic innovations and system architecture designs that focus on reducing data exchange, practical and scalable Edge AI systems can be realized. This research field will continue to grow as it aligns with the broader trends of ubiquitous computing and AI democratization.