Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks

Feng, Chenyuan; Yang, Howard H.; Hu, Deshun; Zhao, Zhiwei; Quek, Tony Q. S.; Min, Geyong

doi:10.1109/twc.2022.3166386

Cited by 64 publications

(17 citation statements)

References 24 publications

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“…For instance, one can explore the effects of adopting multiple antennas at the AP, which can be used to manoeuvre power boosting and/or interference cancellation [25], on the performance of SFWFL. Investigating the impacts of UEs' mobility on the system's performance is also a concrete direction [26]. Another future extension of the present study is to reduce the sensitivity of SFWFL to heavy-tailed interference via, e.g., the gradient clipping schemes [27] APPENDIX…”

Section: Discussionmentioning

confidence: 99%

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Server Free Wireless Federated Learning: Architecture, Algorithm, and Analysis

Yang¹,

Chen²,

Quek³

2022

Preprint

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We demonstrate that merely analog transmissions and match filtering can realize the function of an edge server in federated learning (FL). Therefore, a network with massively distributed user equipments (UEs) can achieve large-scale FL without an edge server. We also develop a training algorithm that allows UEs to continuously perform local computing without being interrupted by the global parameter uploading, which exploits the full potential of UEs' processing power. We derive convergence rates for the proposed schemes to quantify their training efficiency. The analyses reveal that when the interference obeys a Gaussian distribution, the proposed algorithm retrieves the convergence rate of a server-based FL. But if the interference distribution is heavy-tailed, then the heavier the tail, the slower the algorithm converges. Nonetheless, the system run time can be largely reduced by enabling computation in parallel with communication, whereas the gain is particularly pronounced when communication latency is high. These findings are corroborated via excessive simulations.

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Section: Discussionmentioning

confidence: 99%

“…The model training procedure in Algorithm 2 stipulates the following relationship:∆k+1 = ∆k − η k N n=1 ḡn k + η k−D 1 N N n=1 ḡn k−D − gk−D . (34)Taking similar steps as(26), we arrive at the following:E ∆k+1 α α ≤ E ∆k − η k M ∇f ( wk )…”

mentioning

confidence: 95%

Server Free Wireless Federated Learning: Architecture, Algorithm, and Analysis

Yang¹,

Chen²,

Quek³

2022

Preprint

Self Cite

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“…To train the AI models, we incorporate federated learning (FL) algorithm [1], [2], as shown in Fig. 3 Furthermore, multiple BSs can jointly train the model in a wider range using the hierarchical FL algorithm [11], where a central parameter server aggregates the models from BSs periodically.…”

Section: B Timely Edge Training With Opvsmentioning

confidence: 99%

“…A case study is shown in Section VI. For hierarchical FL, the central aggregation rule also needs to be designed based on the distribution and mobility of OPVs [11].…”

Section: ) Scheduling Policies For Timely Edge Trainingmentioning

confidence: 99%

MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks

et al. 2023

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Edge intelligence is an emerging paradigm for real-time training and inference at the wireless edge, thus enabling mission-critical applications. Accordingly, base stations (BSs) and edge servers (ESs) need to be densely deployed, leading to huge deployment and operation costs, in particular the energy costs. In this article, we propose a new framework called Mobility-Enhanced Edge inTelligence (MEET), which exploits the sensing, communication, computing, and self-powering capabilities of intelligent connected vehicles for the smart and green 6G networks. Specifically, the operators can incorporate infrastructural vehicles as movable BSs or ESs, and schedule them in a more flexible way to align with the communication and computation traffic fluctuations. Meanwhile, the remaining compute resources of opportunistic vehicles are exploited for edge training and inference, where mobility can further enhance edge intelligence by bringing more compute resources, communication opportunities, and diverse data.In this way, the deployment and operation costs are spread over the vastly available vehicles, so that the edge intelligence is realized cost-effectively and sustainably. Furthermore, these vehicles can be either powered by renewable energy to reduce carbon emissions, or charged more flexibly during off-peak hours to cut electricity bills. I. INTRODUCTION6G networks are expected to support numerous mission-critical applications, such as autonomous driving, smart city, and industrial Internet of things. Artificial intelligence (AI)-based

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“…How to preserve the positive gains while avoiding undesired degradation during scaling to hierarchical architectures remains an active research topic. While previous works have studied how to improve FL convergence under one or two of data heterogeneity [31,39,50], system heterogeneity [13,34,38], unexpected stragglers [44], and hierarchical FL for better scalability [21,62], none of existing work provides a systematic solution to address all challenges in a hierarchical and unreliable IoT network. Our work is the first end-to-end framework that uses (i) asynchronous and hierarchical FL algorithm and (ii) system management design to enhance efficiency and robustness, for handling all challenges (C1)-(C4).…”

Section: Introductionmentioning

confidence: 99%

Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks

Yu¹,

Cherkasova²,

Vardhan³

et al. 2023

Preprint

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Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deploying FL in real-world Internet-of-Things (IoT) networks with hierarchies. Although existing works have proposed various approaches to account data heterogeneity, system heterogeneity, unexpected stragglers and scalibility, none of them provides a systematic solution to address all of the challenges in a hierarchical and unreliable IoT network. In this paper, we propose an asynchronous and hierarchical framework (Async-HFL) for performing FL in a common three-tier IoT network architecture. In response to the largely varied delays, Async-HFL employs asynchronous aggregations at both the gateway and the cloud levels thus avoids long waiting time. To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level. Device selection chooses edge devices to trigger local training in real-time while device-gateway association determines the network topology periodically after several cloud epochs, both satisfying bandwidth limitation. We evaluate Async-HFL's convergence speedup using large-scale simulations based on ns-3 and a network topology from NYCMesh. Our results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and saves up to 21.6% total communication cost compared to state-of-the-art asynchronous FL algorithms (with client selection). We further validate Async-HFL on a physical deployment and observe robust convergence under unexpected stragglers.

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Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks

Cited by 64 publications

References 24 publications

Server Free Wireless Federated Learning: Architecture, Algorithm, and Analysis

Server Free Wireless Federated Learning: Architecture, Algorithm, and Analysis

MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks

Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks

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