169 research outputs found
Federated Learning and Meta Learning: Approaches, Applications, and Directions
Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data is collected at a central server for training. However, this approach poses a challenge in terms of preserving the data privacy of devices. To address this issue, federated learning (FL) has emerged as an effective solution that allows edge devices to collaboratively train ML models without compromising data privacy. In FL, local datasets are not shared, and the focus is on learning a global model for a specific task involving all devices. However, FL has limitations when it comes to adapting the model to devices with different data distributions. In such cases, meta learning is considered, as it enables the adaptation of learning models to different data distributions using only a few data samples. In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks. We also analyze the relationships among these learning algorithms and examine their advantages and disadvantages in real-world applications
Federated Learning and Meta Learning:Approaches, Applications, and Directions
Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches rely on centralized methods, where data is collected at a central server for training. However, this approach poses a challenge in terms of preserving the data privacy of devices. To address this issue, federated learning (FL) has emerged as an effective solution that allows edge devices to collaboratively train ML models without compromising data privacy. In FL, local datasets are not shared, and the focus is on learning a global model for a specific task involving all devices. However, FL has limitations when it comes to adapting the model to devices with different data distributions. In such cases, meta learning is considered, as it enables the adaptation of learning models to different data distributions using only a few data samples. In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks. We also analyze the relationships among these learning algorithms and examine their advantages and disadvantages in real-world applications.</p
A Family of Likelihood Ascent Search Multiuser Detectors: an Upper Bound of Bit Error Rate and a Lower Bound of Asymptotic Multiuser Efficiency
In this paper, the bit error performance of a family of likelihood ascent
search (LAS) multiuser detectors is analyzed. An upper bound on the BER of any
LAS detector is obtained by bounding the fixed point region with the worst
initial detector. The concept of indecomposable errors developed by Verdu is
applied to tighten the upper bound. In a special instance, the upper bound is
reduced to that for all the local maximum likelihood detectors. The upper bound
is comparable with that of the optimum detector obtained by Verdu. A lower
bound on the asymptotic multiuser efficiency (AME) is then obtained. It is
shown that there are nontrivial CDMA channels such that a LAS detector can
achieve unit AME regardless of user number. The AME lower bound provides a
means for further seeking a good set of spreading sequences and power
distribution for spectral and power efficient CDMA.Comment: To appear in IEEE Trans. on Communication
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