Edge Intelligence Over the Air: Two Faces of Interference in Federated
Learning
- URL: http://arxiv.org/abs/2306.10299v1
- Date: Sat, 17 Jun 2023 09:04:48 GMT
- Title: Edge Intelligence Over the Air: Two Faces of Interference in Federated
Learning
- Authors: Zihan Chen, Howard H. Yang, Tony Q. S. Quek
- Abstract summary: Federated edge learning is envisioned as the bedrock of enabling intelligence in next-generation wireless networks.
This article provides a comprehensive overview of the positive and negative effects of interference on over-the-air-based edge learning systems.
- Score: 95.31679010587473
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Federated edge learning is envisioned as the bedrock of enabling intelligence
in next-generation wireless networks, but the limited spectral resources often
constrain its scalability. In light of this challenge, a line of recent
research suggested integrating analog over-the-air computations into federated
edge learning systems, to exploit the superposition property of electromagnetic
waves for fast aggregation of intermediate parameters and achieve (almost)
unlimited scalability. Over-the-air computations also benefit the system in
other aspects, such as low hardware cost, reduced access latency, and enhanced
privacy protection. Despite these advantages, the interference introduced by
wireless communications also influences various aspects of the model training
process, while its importance is not well recognized yet. This article provides
a comprehensive overview of the positive and negative effects of interference
on over-the-air computation-based edge learning systems. The potential open
issues and research trends are also discussed.
Related papers
- Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS)
FLEKD enables a more flexible aggregation method than conventional model fusion techniques.
Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
arXiv Detail & Related papers (2024-01-22T14:16:37Z) - Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar
Data Processing [4.359030177348051]
This paper presents novel techniques that leverage the temporal correlation present in streaming radar data to enhance the efficiency of Early Exit Neural Networks for Deep Learning inference on embedded devices.
Our results demonstrate that our techniques save up to 26% of operations per inference over a Single Exit Network and 12% over a confidence-based Early Exit version.
Such efficiency gains enable real-time radar data processing on resource-constrained platforms, allowing for new applications in the context of smart homes, Internet-of-Things, and human-computer interaction.
arXiv Detail & Related papers (2023-09-11T12:38:01Z) - Practical Insights on Incremental Learning of New Human Physical
Activity on the Edge [1.494944639485053]
We focus on the intricacies of Edge-based learning, examining the interdependencies among: (i) constrained data storage on Edge devices, (ii) limited computational power for training, and (iii) the number of learning classes.
arXiv Detail & Related papers (2023-08-22T16:40:09Z) - Measuring and Mitigating Interference in Reinforcement Learning [30.38857177546063]
Catastrophic interference is common in many network-based learning systems.
We provide a definition and novel measure of interference for value-based reinforcement learning methods.
arXiv Detail & Related papers (2023-07-10T20:20:20Z) - Collaborative Learning over Wireless Networks: An Introductory Overview [84.09366153693361]
We will mainly focus on collaborative training across wireless devices.
Many distributed optimization algorithms have been developed over the last decades.
They provide data locality; that is, a joint model can be trained collaboratively while the data available at each participating device remains local.
arXiv Detail & Related papers (2021-12-07T20:15:39Z) - Distributed Learning in Wireless Networks: Recent Progress and Future
Challenges [170.35951727508225]
Next-generation wireless networks will enable many machine learning (ML) tools and applications to analyze various types of data collected by edge devices.
Distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges.
This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks.
arXiv Detail & Related papers (2021-04-05T20:57:56Z) - Towards AIOps in Edge Computing Environments [60.27785717687999]
This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments.
It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
arXiv Detail & Related papers (2021-02-12T09:33:00Z) - Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified
Communication-Learning Design Approach [30.1988598440727]
We develop a unified communication-learning optimization problem to jointly optimize device selection, over-the-air transceiver design, and RIS configuration.
Numerical experiments show that the proposed design achieves substantial learning accuracy improvement compared with the state-of-the-art approaches.
arXiv Detail & Related papers (2020-11-20T08:54:13Z) - Truly Intelligent Reflecting Surface-Aided Secure Communication Using
Deep Learning [32.34501171201543]
This paper considers machine learning for physical layer security design for communication in a challenging wireless environment.
A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time.
arXiv Detail & Related papers (2020-04-07T00:48:58Z) - Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G
Networks [84.2155885234293]
We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC.
To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
arXiv Detail & Related papers (2020-02-22T14:38:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.