Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration

@article{Liu2022TowardAI,
  title={Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration},
  author={Peixi Liu and Guangxu Zhu and Shuai Wang and Wei Jiang and Wu Luo and H. Vincent Poor and Shuguang Cui},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.05949}
}
—With the breakthroughs in deep learning and con- tactless sensors, the recent years have witnessed a rise of ambient intelligence applications and services, spanning from healthcare delivery to intelligent home. Federated edge learning (FEEL), as a privacy-enhancing paradigm of collaborative learning at the network edge, is expected to be the core engine to achieve ambient intelligence. Sensing, computation, and communication (SC 2 ) are highly coupled processes in FEEL and need to be jointly… 

Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G

A timely overview of ISCC for edge intelligence is provided by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead.

Integrated Sensing, Computation, and Communication: System Framework and Performance Optimization

This work proposes ancient sensing framework with a novel action detection module that can reduce the overhead of computation resource by detecting whether the sensing target is static and formulate a sensing accuracy maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks.

References

SHOWING 1-10 OF 38 REFERENCES

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

The vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models is provided and new design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described.

Federated Learning via Over-the-Air Computation

A novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel and providing a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection.

WiFederated: Scalable WiFi Sensing Using Edge-Based Federated Learning

This work proposes WiFederated, a federated learning (FL) approach to train machine learning models for WiFi sensing tasks that provides a more accurate and time-efficient solution compared to existing transfer learning and adversarial learning solutions thanks to the parallel training ability at multiple clients.

Distributed Learning in Wireless Networks: Recent Progress and Future Challenges

This paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning.

Optimized Power Control Design for Over-the-Air Federated Edge Learning

This paper investigates the transmission power control to combat against aggregation errors in Air-FEEL and proposes a new power control design aiming at directly maximizing the convergence speed, using the Lagrangian duality method.

Adaptive Batch Size for Federated Learning in Resource-Constrained Edge Computing

This work proposes an efficient algorithm that adaptively adjusts batch size with scaled learning rate for heterogeneous devices to reduce the waiting time and save battery life and theoretically analyzes the convergence rate of global model to obtain a convergence upper bound.

Federated Learning Over Wireless Fading Channels

Results show clear advantages for the proposed analog over-the-air DSGD scheme, which suggests that learning and communication algorithms should be designed jointly to achieve the best end-to-end performance in machine learning applications at the wireless edge.

Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification

Network and training frameworks for FLCAV are presented and multi-layer graph resource allocation and vehicle-road pose contrastive methods are proposed to address the network management and sensor pose problems, respectively.

Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends, and Challenges

An attempt to introduce a definition of ISAC, analyze the various influencing forces, and present several novel use cases, which complement the understanding of the signaling layer by presenting several key benefits in the IoT era.

Energy Efficient Federated Learning Over Wireless Communication Networks

An iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived and can reduce up to 59.5% energy consumption compared to the conventional FL method.