Federated Learning over Energy Harvesting Wireless Networks
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In this article, a joint energy management and user scheduling problem in federated learning over wireless systems is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling.Abstract:
In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) employs massive multiple-input multiple-output (MIMO) to serve a set of users powered by independent energy harvesting sources. Since a certain number of users may not be able to participate in FL due to the interference and energy constraints, a joint energy management and user scheduling problem in FL over wireless systems is formulated. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To find how the factors such as transmit power and number of scheduled users affect the training loss, the convergence rate of the FL algorithm is first analyzed. Given this analytical result, the user scheduling and energy management optimization problem can be decomposed, simplified, and solved. Further, the system model is extended by considering multiple BSs. Hence, a joint user association and scheduling problem in FL over wireless systems is studied. The optimal user association problem is solved using the branch-and-bound technique. Simulation results show that the proposed user scheduling and user association algorithm can reduce training loss compared to a standard FL algorithm.read more
Citations
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References
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Journal ArticleDOI
Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays
Fredrik Rusek,Daniel Persson,Buon Kiong Lau,Erik G. Larsson,Thomas L. Marzetta,Fredrik Tufvesson +5 more
TL;DR: The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time.
Journal ArticleDOI
Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels
TL;DR: While the proposed algorithms are suboptimal, they lead to simpler transmitter and receiver structures and allow for a reasonable tradeoff between performance and complexity.
Journal ArticleDOI
Federated Learning: Challenges, Methods, and Future Directions
TL;DR: In this paper, the authors discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.
Journal ArticleDOI
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Shiqiang Wang,Tiffany Tuor,Theodoros Salonidis,Kin K. Leung,Christian Makaya,Ting He,Kevin S. Chan +6 more
TL;DR: In this paper, the authors consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place, and propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget.
Proceedings ArticleDOI
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Takayuki Nishio,Ryo Yonetani +1 more
TL;DR: In this paper, a federated learning (FL) protocol for heterogeneous clients in a mobile edge computing (MEC) network is proposed. But the authors consider the problem of data aggregation in the overall training process and propose a new protocol to solve it.