Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks

Abad, Mehdi Salehi Heydar; Ozfatura, Emre; Gündüz, Deniz; Erçetin, Özgür

doi:10.1109/icassp40776.2020.9054634

Cited by 248 publications

(151 citation statements)

References 7 publications

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“…Several studies have considered resource optimization in federated learning [14], [17]- [19]. For example, in [17], resource optimization and incentive mechanism for federated learning at the network edge were presented.…”

Section: Related Workmentioning

confidence: 99%

“…As a future work, one can extend the work for multiple BSs with efficient solutions. On the other hand, hierarchical federated learning has been proposed to offer wireless resource optimization [14]. A heterogeneous cellular network consisting of a macro base station (MBS) and small cell base stations (SBS) was considered.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the global model updates are sent to the SBS that broadcast the global model parameters to the end devices. The work presented in [14] an attractive solution that can reuse wireless resources. However, their scheme uses centralized MBS for global aggregation which might be out of order due to physical damage.…”

Section: Related Workmentioning

confidence: 99%

“…Another feasible way is to use light-weight authentication scheme for learning models verification. To tackle the issue of high communication resource usage, we can use the hierarchical fashion for collaborative FL to offer communication resource optimization [14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

et al. 2020

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Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and machine learning schemes for industries can assist in streamlining manufacturing processes, revolutionizing operational analytics, and maintaining factory efficiency. However, further adoption of centralized machine learning in industries seems to be restricted due to data privacy issues. Federated learning has the potential to bring about predictive features in industrial systems without leaking private information. However, its implementation involves key challenges including resource optimization, robustness, and security. In this paper, we propose a novel dispersed federated learning (DFL) framework to provide resource optimization, whereby distributed fashion of learning offers robustness. We formulate an integer linear optimization problem to minimize the overall federated learning cost for the DFL framework. To solve the formulated problem, first, we decompose it into two sub-problems: association and resource allocation problem. Second, we relax the association and resource allocation sub-problems to make them convex optimization problems. Later, we use the rounding technique to obtain binary association and resource allocation variables. Our proposed algorithm works in an iterative manner by fixing one problem variable (for example, association) and compute the other (for example, resource allocation). The iterative algorithm continues until convergence of the formulated cost optimization problem. Furthermore, we compare the proposed DFL with two schemes; namely, random resource allocation and random association. Numerical results show the superiority of the proposed DFL scheme. INDEX TERMS Smart industry, cognitive Internet of Things, federated learning, convex optimization.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

et al. 2020

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“…Belonging to the first category, gradient quantization [3] and gradient sparsification [4,5] can be used to improve the communication efficiency. Besides, two ways to reduce the uplink communication costs: structured updates and sketched updates of model parameters were proposed in [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Armed Bandit-Based Client Scheduling for Federated Learning

Xia

Quek

Guo

et al. 2020

IEEE Trans. Wireless Commun.

220

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By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms.

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Federated learning and next generation wireless communications: A survey on bidirectional relationship

Shome

Waqar

Khan

2022

Trans Emerging Tel Tech

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In order to meet the extremely heterogeneous requirements of the next generation wireless communication networks, research community is increasingly dependent on using machine‐learning solutions for real‐time decision‐making and radio resource management. Traditional machine learning employs fully centralized architecture in which the entire training data is collected at one node for example, cloud server, that significantly increases the communication overheads and also raises severe privacy concerns. Toward this end, a distributed machine‐learning paradigm termed as federated learning (FL) has been proposed recently. In FL, each participating edge device trains its local model by using its own training data. Then, via the wireless channels the weights or parameters of the locally trained models are sent to the central parameter server (PS), that aggregates them and updates the global model. On one hand, FL plays an important role for optimizing the resources of wireless communication networks, on the other hand, wireless communications is crucial for FL. Thus, a “bidirectional” relationship exists between FL and wireless communications. Although FL is an emerging concept, many publications have already been published in the domain of FL and its applications for next generation wireless networks. Nevertheless, we noticed that none of the works have highlighted the bidirectional relationship between FL and wireless communications. Therefore, the purpose of this survey article is to bridge this gap in literature by providing a timely and comprehensive discussion on the interdependency between FL and wireless communications.

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Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks

Cited by 248 publications

References 7 publications

Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries

Multi-Armed Bandit-Based Client Scheduling for Federated Learning

Federated learning and next generation wireless communications: A survey on bidirectional relationship

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