Federated Learning in Mobile Edge Networks: A Comprehensive Survey

By Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusist Niyato, Chunyan Miao. 2019

2,306 citations recorded by the source corpus.

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing 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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization (2020, background): “Federated learning [1–5] is an emerging sub-area of distributed optimization where both data collection and model training is pushed to a large number of edge clients that have limited communication and computation capabilities.”
From distributed machine learning to federated learning: a survey (2021, background): “Also, some userswith unstablewireless communication channels may consequently drop out due to disconnection from the Internet [99].”
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning (2020, background): “A typical wireless FL system leverages the computation capabilities of multiple end devices, which are coordinated by a central controller, for example a base station (BS), to train a model in an iterative fashion [8].”
Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, methodology): “[129] study federated learning in mobile edge networks and divided the existing methods into methods that solve the fundamental problems of federated learning and methods that use federated learning to solve edge computing problems.”
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2020, background): “For a comprehensive survey in this area, we refer the readers to [45].”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, methodology): “In Section III, we describe the threat models and then explain the motivations behind the integration of FL and blockchain.”
Asynchronous Federated Learning on Heterogeneous Devices: A Survey (2021, methodology): “with classic ML approaches, FL presents a range of merits specifically tailored to edge applications [14, 18]: (1) Enhanced preservation of local data privacy, facilitated by the gradientsbased aggregation of the global model; (2) Reduced network transmission latency, as the training data remains lo”
Federated Learning in Edge Computing: A Systematic Survey (2022, methodology): “Back-propagation is used to deduce gradient matrices from the gradient error input e described [49,50] as e = ∂L B ∂y .”
Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning (2020, positive): “To account for the statistical heterogeneity across users, several studies have emerged with effective frameworks, e.g., through concepts borrowed from multi-task learning in which each user learns personalized models to ensure that high inference accuracy is retained [2].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “Several possible selection factors for this selection can be channel conditions and the importance of local updates of each IoT device [17].”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “FL Challenges of FL implementation [26]”
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020, background): “Existing federated learning approaches mainly focus on the communication rounds reduction considering the fact that mobile devices are frequently ofﬂine [8].”
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, neutral): “2020 [15] Present the implementation challenges of federated learning at mobile edge networks 3 7 3”
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning (2021, methodology): “To improve data privacy for ML training, federated learning (FL) is introduced by allowing devices at the edge, such as smartphones, to collaboratively train a shared ML model while keeping all user data locally on the device [11, 34, 52, 64, 71, 77, 84, 116, 120, 135, 137].”
Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, neutral): “Considering the requirements of computing power, data transmission, and personal privacy, system deployments are increasingly inclined to store data locally, and model calculations are performed by edge devices [120].”