In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (1809.07857v2)

Abstract: Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attentions from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate the content deliveries and improve the quality of mobile services. In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication. And thus, we design the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. "In-Edge AI" is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to the mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of "In-Edge AI".

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

The paper "In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning" by Xiaofei Wang et al. introduces an innovative framework "In-Edge AI" that leverages Federated Learning (FL) and Deep Reinforcement Learning (DRL) to enhance the efficiency and intelligence of Mobile Edge Computing (MEC) systems. This research addresses the increasing demands for multimedia services in mobile networks and the resultant surge in data traffic and computational load on backbone networks and clouds.

Overview of the In-Edge AI Framework

The framework aims to optimize three key components of MEC: computing, caching, and communication. Traditional optimization methods, including convex optimization and game theory, have limitations in terms of addressing uncertain inputs, dynamic systems, and temporal isolation effects in MEC environments. Instead, the proposed approach integrates DRL and FL to provide a more adaptive, robust, and efficient solution.

Use Cases

1. Edge Caching: The DRL model in edge nodes makes dynamic caching decisions based on content requests from User Equipments (UEs). The system manages a content library with defined popularity distributions, optimizing cache replacements to improve hit rates. A key challenge tackled is the cooperation among multiple edge nodes to adapt to fluctuating content popularity.
2. Computation Offloading: UEs decide whether to offload tasks to edge nodes or execute them locally. This decision involves selecting an appropriate wireless channel and allocating energy resources, accounting for wireless channel variations and task execution delays. The formulation uses a Markov Decision Process (MDP) to model these dynamics and applies DRL to optimize long-term utility.

Integrating Federated Learning

The introduction of FL aims to overcome some critical challenges in DRL application:

• Non-IID Data: Aggregating model updates from multiple UEs addresses the non-independent and identically distributed (non-IID) nature of the training data.
• Limited Communication: FL reduces the volume of data sent over the network by only transmitting model updates rather than raw data.
• Privacy: By keeping data on local devices and only sharing model updates, FL mitigates privacy concerns associated with data transmission.

Performance Evaluation

The paper provides empirical evaluations through simulations:

1. Edge Caching: The hit rate of cache requests improves significantly with the use of DRL and approaches that of centralized models, outperforming traditional cache replacement policies like LRU, LFU, and FIFO.
2. Computation Offloading: The average utility of UEs in the MEC system increases, demonstrating enhanced task handling efficiency compared to baseline policies such as mobile execution and greedy execution.
3. Training Efficiency: FL shows a near-optimal performance when compared with centralized DRL models. The training process is more communication-efficient, as evidenced by lower transmission costs.

Implications and Future Directions

The proposed "In-Edge AI" framework highlights a paradigm shift in MEC by harnessing edge and federated learning to decentralize AI tasks. This decentralization is advantageous for privacy, scalability, and communication cost but poses challenges regarding computation load on the UEs and edge nodes.

Future Research

1. Real-Time Optimization: Enhancing the real-time response of DRL in MEC systems, particularly for URLLC scenarios in 5G, remains an open challenge.
2. Incentive Models: Developing robust incentive mechanisms for the collaboration among various stakeholders in the MEC ecosystem is crucial.
3. Efficiency Improvements: Investigating methods to balance computation and communication trade-offs, including transfer learning, can further refine the framework's applicability and performance.

Conclusion

The "In-Edge AI" framework presented in this paper offers a comprehensive approach to addressing the limitations of traditional optimization methods in MEC. By integrating DRL with FL, the framework provides a scalable and efficient solution to meet the increasing demands for intelligent mobile services, making a significant contribution to the field of edge computing and AI. Further research and refinement of this framework will be vital to fully realize the potential of MEC systems in future mobile networks.