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2022 | Buch

Federated Learning at Mobile Edge Networks: A Tutorial Kapitel lesen Erstes Kapitel lesen

Federated Learning Over Wireless Edge Networks

verfasst von: Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao

Verlag: Springer International Publishing

Buchreihe : Wireless Networks

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Federated Learning at Mobile Edge Networks: A Tutorial
Abstract
In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning, this opens up countless possibilities for meaningful applications to be developed. Traditional cloud-based Machine Learning approaches require the data to be aggregated in a cloud server or data center. However, this results in critical issues related to data privacy. In light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises the challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this chapter, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review cutting-edge solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.
Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao
Chapter 2. Multi-dimensional Contract Matching Design for Federated Learning in UAV Networks
Abstract
The wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), are becoming increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned UAVs. In this chapter, we propose the adoption of a Federated Learning (FL) based approach to enable privacy-preserving collaborative Machine Learning across a federation of independent DaaS providers for the development of IoV applications, e.g., for traffic prediction. Given the information asymmetry and incentive mismatches between the UAVs and model owners, we leverage on the self-revealing properties of a multi-dimensional contract to ensure truthful reporting of the UAV types, while accounting for the multiple sources of heterogeneity, e.g., in sensing, computation, and transmission costs. Then, we adopt the Gale–Shapley algorithm to match the lowest cost UAV to each subregion. The simulation results validate the incentive compatibility of our contract design and show the efficiency of our matching, thus guaranteeing profit maximization for the model owner amid information asymmetry.
Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao
Chapter 3. Joint Auction–Coalition Formation Framework for UAV-Assisted Communication-Efficient Federated Learning
Abstract
The performance of the Federated Learning suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. In this chapter, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the Internet of Vehicles (IoV) components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. Therefore, we present a joint auction–coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction–coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions. Thereafter, we design an auction scheme to solve the allocation of UAV coalitions. The auction scheme takes into account the preferences of IoV components over heterogeneous UAVs. The simulation results show the communication latency reduction of FL using our proposed scheme.
Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao
Chapter 4. Evolutionary Edge Association and Auction in Hierarchical Federated Learning
Abstract
To reduce node failures and device dropouts, the Hierarchical Federated Learning (HFL) framework has been proposed whereby cluster heads are designated to support the data owners through intermediate model aggregation. This decentralized learning approach reduces the reliance on a central controller, e.g., the model owner. However, the issues of resource allocation and incentive design are not well-studied in the HFL framework. In this chapter, we consider a two-level resource allocation and incentive mechanism design problem. In the lower level, the cluster heads offer rewards in exchange for the data owners’ participation, and the data owners are free to choose which cluster to join. Specifically, we apply the evolutionary game theory to model the dynamics of the cluster selection process. In the upper level, each cluster head can choose to serve a model owner, whereas the model owners have to compete among each other for the services of the cluster heads. As such, we propose a deep learning based auction mechanism to derive the valuation of each cluster head’s services. The performance evaluation shows the uniqueness and stability of our proposed evolutionary game, as well as the revenue maximizing properties of the deep learning based auction.
Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao
Chapter 5. Conclusion and Future Works
Abstract
In this chapter, we revisit the key concepts derived from each chapter in the book. Then, we discuss the future research directions and open issues to solve toward deploying Federated Learning at scale.
Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao
Backmatter
Metadaten
Titel
Federated Learning Over Wireless Edge Networks
verfasst von
Wei Yang Bryan Lim
Jer Shyuan Ng
Zehui Xiong
Dusit Niyato
Chunyan Miao
Copyright-Jahr
2022
Electronic ISBN
978-3-031-07838-5
Print ISBN
978-3-031-07837-8
DOI
https://doi.org/10.1007/978-3-031-07838-5

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