Federated Learning over Wireless Networks: Optimization Model Design and Analysis

Tran, Nguyen H.; Bao, Wei; Zomaya, Albert Y.; Minh, N H Nguyen; Hong, Choong Seon

doi:10.1109/infocom.2019.8737464

Cited by 757 publications

(463 citation statements)

References 10 publications

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“…In addition to this, the framework may suffer from slower convergence due to fewer participation. Thus, MEC will avoid deliberately dropping the clients to achieve a faster consensus with (22). Furthermore, using the relationship defined in (19) between x(ǫ) and relative θ accuracy for the subproblem, we can analyze the impact of responses θ on MEC server's utility in a FL setting with the constraint (11).…”

Section: Mec Server Utility Modelmentioning

confidence: 99%

“…The utility maximization problem in (21) is a convex optimization problem whose optimal solution can be obtained by using Lagrangian duality. The lagrangian of (21) where λ ≥ 0 is the Lagrangian multiplier for constraint (22). By taking the first-order derivative of (A.1) with respect to x(ǫ) and λ, KKT conditions are expressed as follows:…”

Section: Appendix a Kkt Solutionmentioning

confidence: 99%

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A Crowdsourcing Framework for On-Device Federated Learning

Pandey

Tran

Bennis

et al. 2020

IEEE Trans. Wireless Commun.

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213

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Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentivebased interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22% gain in the offered reward.

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Section: Mec Server Utility Modelmentioning

confidence: 99%

Section: Appendix a Kkt Solutionmentioning

confidence: 99%

A Crowdsourcing Framework for On-Device Federated Learning

Pandey

Tran

Bennis

et al. 2020

IEEE Trans. Wireless Commun.

Self Cite

213

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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%

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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“…Conversely, federated learning (especially suitable for mobile networks) aims to distribute data and computation tasks among federated devices that are coordinated by a central server. The server is in charge of combining the local models into a common neural network, which is based on, and updated according to, the local datasets [45]. Finally, the goal of (deep) transfer learning is to apply the knowledge from one task to another in a related context to reduce the amount of data required for training and validating new models [46].…”

Section: Machine Learning Principles and Techniquesmentioning

confidence: 99%

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

2020

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Software-Defined Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time. Beyond simply scrutinizing data, Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defined Networking (KDN). This work discusses the inclusion of ML techniques in the specific case of Software-Defined Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem. With this in mind, we propose an adaptive ML-based approach for frame size selection on a per-user basis by taking into account both specific channel conditions and global performance indicators. By relying on standard frame aggregation mechanisms, the model can be seamlessly embedded into any Enterprise SD-WLAN by obtaining the data needed from the control plane, and then returning the output back to this in order to efficiently adapt the frame size to the needs of each user. Our approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.

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Federated Learning over Wireless Networks: Optimization Model Design and Analysis

Cited by 757 publications

References 10 publications

A Crowdsourcing Framework for On-Device Federated Learning

A Crowdsourcing Framework for On-Device Federated Learning

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

Adaptive ML-Based Frame Length Optimisation in Enterprise SD-WLANs

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